Fix tf.raw_ops.QuantizeAndDequantizeV4Grad vulnerability with invalid input_min or input_max.

Check that argument is actually a scalar before treating it as such.

PiperOrigin-RevId: 445198280

  void Compute(OpKernelContext* ctx) override {
    const Tensor& gradient = ctx->input(0);
    const Tensor& input = ctx->input(1);
    Tensor* input_backprop = nullptr;
    OP_REQUIRES_OK(ctx,
                   ctx->allocate_output(0, input.shape(), &input_backprop));
    OP_REQUIRES(
        ctx, axis_ >= -1,
        errors::InvalidArgument("Axis must be at least -1. Found ", axis_));
    OP_REQUIRES(ctx, (axis_ == -1 || axis_ < input.shape().dims()),
                errors::InvalidArgument(
                    "Axis should be -1 or 0 or a positive value less than ",
                    input.shape().dims(), "but given axis value was ", axis_));

    OP_REQUIRES(
        ctx, input.IsSameSize(gradient),
        errors::InvalidArgument("gradient and input must be the same size"));
    const int depth = (axis_ == -1) ? 1 : input.dim_size(axis_);
    const Tensor& input_min_tensor = ctx->input(2);
    OP_REQUIRES(ctx,
                input_min_tensor.dims() == 0 || input_min_tensor.dims() == 1,
                errors::InvalidArgument(
                    "Input min tensor must have dimension 1. Recieved ",
                    input_min_tensor.dims(), "."));
    const Tensor& input_max_tensor = ctx->input(3);
    OP_REQUIRES(ctx,
                input_max_tensor.dims() == 0 || input_max_tensor.dims() == 1,
                errors::InvalidArgument(
                    "Input max tensor must have dimension 1. Recieved ",
                    input_max_tensor.dims(), "."));
    if (axis_ != -1) {
      OP_REQUIRES(
          ctx, input_min_tensor.dim_size(0) == depth,
          errors::InvalidArgument("min has incorrect size, expected ", depth,
                                  " was ", input_min_tensor.dim_size(0)));
      OP_REQUIRES(
          ctx, input_max_tensor.dim_size(0) == depth,
          errors::InvalidArgument("max has incorrect size, expected ", depth,
                                  " was ", input_max_tensor.dim_size(0)));
    }

    TensorShape min_max_shape(input_min_tensor.shape());
    Tensor* input_min_backprop;
    OP_REQUIRES_OK(ctx,
                   ctx->allocate_output(1, min_max_shape, &input_min_backprop));

    Tensor* input_max_backprop;
    OP_REQUIRES_OK(ctx,
                   ctx->allocate_output(2, min_max_shape, &input_max_backprop));

    if (axis_ == -1) {
      functor::QuantizeAndDequantizeOneScaleGradientFunctor<Device, T> f;
      f(ctx->eigen_device<Device>(), gradient.template flat<T>(),
        input.template flat<T>(), input_min_tensor.scalar<T>(),
        input_max_tensor.scalar<T>(), input_backprop->template flat<T>(),
        input_min_backprop->template scalar<T>(),
        input_max_backprop->template scalar<T>());
    } else {
      functor::QuantizeAndDequantizePerChannelGradientFunctor<Device, T> f;
      f(ctx->eigen_device<Device>(),
        gradient.template flat_inner_outer_dims<T, 3>(axis_ - 1),
        input.template flat_inner_outer_dims<T, 3>(axis_ - 1),
        &input_min_tensor, &input_max_tensor,
        input_backprop->template flat_inner_outer_dims<T, 3>(axis_ - 1),
        input_min_backprop->template flat<T>(),
        input_max_backprop->template flat<T>());
    }
  }

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.QuantizeAndDequantizeV4Grad` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.


Fix wolfmqtt-fuzzer: Null-dereference WRITE in MqttProps_Free

static int MqttClient_WaitType(MqttClient *client, void *packet_obj,
    byte wait_type, word16 wait_packet_id, int timeout_ms)
{
    int rc;
    word16 packet_id;
    MqttPacketType packet_type;
#ifdef WOLFMQTT_MULTITHREAD
    MqttPendResp *pendResp;
    int readLocked;
#endif
    MqttMsgStat* mms_stat;
    int waitMatchFound;

    if (client == NULL || packet_obj == NULL) {
        return MQTT_CODE_ERROR_BAD_ARG;
    }

    /* all packet type structures must have MqttMsgStat at top */
    mms_stat = (MqttMsgStat*)packet_obj;

wait_again:

    /* initialize variables */
    packet_id = 0;
    packet_type = MQTT_PACKET_TYPE_RESERVED;
#ifdef WOLFMQTT_MULTITHREAD
    pendResp = NULL;
    readLocked = 0;
#endif
    waitMatchFound = 0;

#ifdef WOLFMQTT_DEBUG_CLIENT
    PRINTF("MqttClient_WaitType: Type %s (%d), ID %d",
        MqttPacket_TypeDesc((MqttPacketType)wait_type),
            wait_type, wait_packet_id);
#endif

    switch ((int)*mms_stat)
    {
        case MQTT_MSG_BEGIN:
        {
        #ifdef WOLFMQTT_MULTITHREAD
            /* Lock recv socket mutex */
            rc = wm_SemLock(&client->lockRecv);
            if (rc != 0) {
                PRINTF("MqttClient_WaitType: recv lock error!");
                return rc;
            }
            readLocked = 1;
        #endif

            /* reset the packet state */
            client->packet.stat = MQTT_PK_BEGIN;
        }
        FALL_THROUGH;

    #ifdef WOLFMQTT_V5
        case MQTT_MSG_AUTH:
    #endif
        case MQTT_MSG_WAIT:
        {
        #ifdef WOLFMQTT_MULTITHREAD
            /* Check to see if packet type and id have already completed */
            pendResp = NULL;
            rc = wm_SemLock(&client->lockClient);
            if (rc == 0) {
                if (MqttClient_RespList_Find(client, (MqttPacketType)wait_type,
                    wait_packet_id, &pendResp)) {
                    if (pendResp->packetDone) {
                        /* pending response is already done, so return */
                        rc = pendResp->packet_ret;
                    #ifdef WOLFMQTT_DEBUG_CLIENT
                        PRINTF("PendResp already Done %p: Rc %d", pendResp, rc);
                    #endif
                        MqttClient_RespList_Remove(client, pendResp);
                        wm_SemUnlock(&client->lockClient);
                        wm_SemUnlock(&client->lockRecv);
                        return rc;
                    }
                }
                wm_SemUnlock(&client->lockClient);
            }
            else {
                break; /* error */
            }
        #endif /* WOLFMQTT_MULTITHREAD */

            *mms_stat = MQTT_MSG_WAIT;

            /* Wait for packet */
            rc = MqttPacket_Read(client, client->rx_buf, client->rx_buf_len,
                    timeout_ms);
            /* handle failure */
            if (rc <= 0) {
                break;
            }

            /* capture length read */
            client->packet.buf_len = rc;

            /* Decode Packet - get type and id */
            rc = MqttClient_DecodePacket(client, client->rx_buf,
                client->packet.buf_len, NULL, &packet_type, NULL, &packet_id);
            if (rc < 0) {
                break;
            }

        #ifdef WOLFMQTT_DEBUG_CLIENT
            PRINTF("Read Packet: Len %d, Type %d, ID %d",
                client->packet.buf_len, packet_type, packet_id);
        #endif

            *mms_stat = MQTT_MSG_READ;
        }
        FALL_THROUGH;

        case MQTT_MSG_READ:
        case MQTT_MSG_READ_PAYLOAD:
        {
            MqttPacketType use_packet_type;
            void* use_packet_obj;

        #ifdef WOLFMQTT_MULTITHREAD
            readLocked = 1; /* if in this state read is locked */
        #endif

            /* read payload state only happens for publish messages */
            if (*mms_stat == MQTT_MSG_READ_PAYLOAD) {
                packet_type = MQTT_PACKET_TYPE_PUBLISH;
            }

            /* Determine if we received data for this request */
            if ((wait_type == MQTT_PACKET_TYPE_ANY ||
                 wait_type == packet_type ||
                 MqttIsPubRespPacket(packet_type) == MqttIsPubRespPacket(wait_type)) &&
               (wait_packet_id == 0 || wait_packet_id == packet_id))
            {
                use_packet_obj = packet_obj;
                waitMatchFound = 1;
            }
            else {
                /* use generic packet object */
                use_packet_obj = &client->msg;
            }
            use_packet_type = packet_type;

        #ifdef WOLFMQTT_MULTITHREAD
            /* Check to see if we have a pending response for this packet */
            pendResp = NULL;
            rc = wm_SemLock(&client->lockClient);
            if (rc == 0) {
                if (MqttClient_RespList_Find(client, packet_type, packet_id,
                                                               &pendResp)) {
                    /* we found packet match this incoming read packet */
                    pendResp->packetProcessing = 1;
                    use_packet_obj = pendResp->packet_obj;
                    use_packet_type = pendResp->packet_type;
                    /* req from another thread... not a match */
                    waitMatchFound = 0;
                }
                wm_SemUnlock(&client->lockClient);
            }
            else {
                break; /* error */
            }
        #endif /* WOLFMQTT_MULTITHREAD */

            /* Perform packet handling for publish callback and QoS */
            rc = MqttClient_HandlePacket(client, use_packet_type,
                use_packet_obj, timeout_ms);

        #ifdef WOLFMQTT_NONBLOCK
            if (rc == MQTT_CODE_CONTINUE) {
                /* we have received some data, so keep the recv
                    mutex lock active and return */
                return rc;
            }
        #endif

            /* handle success case */
            if (rc >= 0) {
                rc = MQTT_CODE_SUCCESS;
            }

        #ifdef WOLFMQTT_MULTITHREAD
            if (pendResp) {
                /* Mark pending response entry done */
                if (wm_SemLock(&client->lockClient) == 0) {
                    pendResp->packetDone = 1;
                    pendResp->packet_ret = rc;
                #ifdef WOLFMQTT_DEBUG_CLIENT
                    PRINTF("PendResp Done %p", pendResp);
                #endif
                    pendResp = NULL;
                    wm_SemUnlock(&client->lockClient);
                }
            }
        #endif /* WOLFMQTT_MULTITHREAD */
            break;
        }

        case MQTT_MSG_WRITE:
        case MQTT_MSG_WRITE_PAYLOAD:
        default:
        {
        #ifdef WOLFMQTT_DEBUG_CLIENT
            PRINTF("MqttClient_WaitType: Invalid state %d!", *mms_stat);
        #endif
            rc = MQTT_CODE_ERROR_STAT;
            break;
        }
    } /* switch (*mms_stat) */

#ifdef WOLFMQTT_NONBLOCK
    if (rc != MQTT_CODE_CONTINUE)
#endif
    {
        /* reset state */
        *mms_stat = MQTT_MSG_BEGIN;
    }

#ifdef WOLFMQTT_MULTITHREAD
    if (readLocked) {
        wm_SemUnlock(&client->lockRecv);
    }
#endif
    if (rc < 0) {
    #ifdef WOLFMQTT_DEBUG_CLIENT
        PRINTF("MqttClient_WaitType: Failure: %s (%d)",
            MqttClient_ReturnCodeToString(rc), rc);
    #endif
        return rc;
    }

    if (!waitMatchFound) {
        /* if we get here, then the we are still waiting for a packet */
        goto wait_again;
    }

    return rc;
}

wolfSSL wolfMQTT 1.9 has a heap-based buffer overflow in MqttDecode_Disconnect (called from MqttClient_DecodePacket and MqttClient_WaitType).


Fixed frame allocation from an awaited frame.

njs_function_frame_save() is used to save the awaited frame when "await"
instruction is encountered. The saving was done as a memcpy() of
existing runtime frame.

njs_function_frame_alloc() is used to alloc a new function frame, this
function tries to use a spare preallocated memory from the previous
frame first.  Previously, this function might result in "use-after-free"
when invoked from a restored frame saved with njs_function_frame_save().
Because njs_function_frame_save() left pointers to the spare memory of
the original frame which may be already free when saved frame is
restored.

The fix is to erase fields for the spare memory from the saved frame.

This closes #469 issue on Github.

njs_function_frame_save(njs_vm_t *vm, njs_frame_t *frame, u_char *pc)
{
    size_t              value_count, n;
    njs_value_t         *start, *end, *p, **new, *value, **local;
    njs_function_t      *function;
    njs_native_frame_t  *active, *native;

    *frame = *vm->active_frame;
    frame->previous_active_frame = NULL;

    native = &frame->native;

    active = &vm->active_frame->native;
    value_count = njs_function_frame_value_count(active);

    function = active->function;

    new = (njs_value_t **) ((u_char *) native + NJS_FRAME_SIZE);
    value = (njs_value_t *) (new + value_count
                             + function->u.lambda->temp);

    native->arguments = value;
    native->arguments_offset = value + (function->args_offset - 1);
    native->local = new + njs_function_frame_args_count(active);
    native->temp = new + value_count;
    native->pc = pc;

    start = njs_function_frame_values(active, &end);
    p = native->arguments;

    while (start < end) {
        *p = *start++;
        *new++ = p++;
    }

    /* Move all arguments. */

    p = native->arguments;
    local = native->local + function->args_offset;

    for (n = 0; n < function->args_count; n++) {
        if (!njs_is_valid(p)) {
            njs_set_undefined(p);
        }

        *local++ = p++;
    }

    return NJS_OK;
}

nginx njs 0.7.2 is affected suffers from Use-after-free in njs_function_frame_alloc() when it try to invoke from a restored frame saved with njs_function_frame_save().


Prevent crashes when loading tensor slices with unsupported types.

Also fix the `Tensor(const TensorShape&)` constructor swapping the LOG(FATAL)
messages for the unset and unsupported types.

PiperOrigin-RevId: 392695027
Change-Id: I4beda7db950db951d273e3259a7c8534ece49354

Status TensorSliceReader::GetTensor(
    const string& name, std::unique_ptr<tensorflow::Tensor>* out_tensor) const {
  DataType type;
  TensorShape shape;
  TensorSlice slice;
  {
    mutex_lock l(mu_);
    const TensorSliceSet* tss = gtl::FindPtrOrNull(tensors_, name);
    if (tss == nullptr) {
      return errors::NotFound(name, " not found in checkpoint file");
    }

    if (tss->Slices().size() > 1) {
      // TODO(sherrym): Support multi-slice checkpoints.
      return errors::Unimplemented("Sliced checkpoints are not supported");
    }

    type = tss->type();
    shape = tss->shape();
    slice = tss->Slices().begin()->second.slice;
  }

  std::unique_ptr<tensorflow::Tensor> t(new tensorflow::Tensor(type, shape));
  bool success = false;

#define READER_COPY(dt)                                                  \
  case dt:                                                               \
    success = CopySliceData(name, slice,                                 \
                            t->flat<EnumToDataType<dt>::Type>().data()); \
    break;

  switch (type) {
    READER_COPY(DT_FLOAT);
    READER_COPY(DT_DOUBLE);
    READER_COPY(DT_INT32);
    READER_COPY(DT_UINT8);
    READER_COPY(DT_INT16);
    READER_COPY(DT_INT8);
    READER_COPY(DT_INT64);
    READER_COPY(DT_STRING);
    default:
      return errors::Unimplemented("Data type not supported");
  }
#undef READER_COPY

  if (!success) {
    return errors::NotFound(name, " not found in checkpoint file");
  }
  std::swap(*out_tensor, t);

  return Status::OK();
}

TensorFlow is an open source platform for machine learning. In affected versions an attacker can trigger undefined behavior, integer overflows, segfaults and `CHECK`-fail crashes if they can change saved checkpoints from outside of TensorFlow. This is because the checkpoints loading infrastructure is missing validation for invalid file formats. The fixes will be included in TensorFlow 2.7.0. We will also cherrypick these commits on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.


In tf.raw_ops.BoostedTreesSparseCalculateBestFeatureSplit, limit stat_dim in stats_summary_indices to under stats_dims in stats_summary_shape

PiperOrigin-RevId: 387171191
Change-Id: I83ca8a75b22aa78c037e8b98779da6cced16bfaa

  void Compute(OpKernelContext* const context) override {
    // node_id_range
    const Tensor* node_id_range_t;
    OP_REQUIRES_OK(context, context->input("node_id_range", &node_id_range_t));
    const auto node_id_range = node_id_range_t->vec<int32>();
    const int32_t node_id_first = node_id_range(0);  // inclusive
    const int32_t node_id_last = node_id_range(1);   // exclusive

    const Tensor* stats_summary_indices_t;
    OP_REQUIRES_OK(context, context->input("stats_summary_indices",
                                           &stats_summary_indices_t));
    const auto stats_summary_indices = stats_summary_indices_t->matrix<int32>();
    const int32_t num_sparse_entries = stats_summary_indices_t->dim_size(0);

    const Tensor* stats_summary_values_t;
    OP_REQUIRES_OK(context, context->input("stats_summary_values",
                                           &stats_summary_values_t));
    const auto stats_summary_values = stats_summary_values_t->vec<float>();

    const Tensor* stats_summary_shape_t;
    OP_REQUIRES_OK(
        context, context->input("stats_summary_shape", &stats_summary_shape_t));
    const auto stats_summary_shape = stats_summary_shape_t->vec<int32>();
    const int32_t num_buckets = stats_summary_shape(2) - 1;
    const int32_t stats_dims = stats_summary_shape(3);

    const Tensor* l1_t;
    OP_REQUIRES_OK(context, context->input("l1", &l1_t));
    const auto l1 = l1_t->scalar<float>()();

    const Tensor* l2_t;
    OP_REQUIRES_OK(context, context->input("l2", &l2_t));
    const auto l2 = l2_t->scalar<float>()();

    const Tensor* tree_complexity_t;
    OP_REQUIRES_OK(context,
                   context->input("tree_complexity", &tree_complexity_t));
    const auto tree_complexity = tree_complexity_t->scalar<float>()();

    const Tensor* min_node_weight_t;
    OP_REQUIRES_OK(context,
                   context->input("min_node_weight", &min_node_weight_t));
    const auto min_node_weight = min_node_weight_t->scalar<float>()();

    std::vector<int32> output_node_ids;
    std::vector<float> output_gains;
    std::vector<int32> output_feature_dimensions;
    std::vector<int32> output_thresholds;
    std::vector<float> output_left_node_contribs;
    std::vector<float> output_right_node_contribs;
    std::vector<string> output_split_types;

    FeatureMap f_map;

    int32_t previous_node_id = -1;
    for (int idx = 0; idx < num_sparse_entries; ++idx) {
      int32_t node_id = stats_summary_indices(idx, 0);
      if (node_id != previous_node_id) {
        process_node(f_map, &output_node_ids, &output_gains,
                     &output_feature_dimensions, &output_thresholds,
                     &output_left_node_contribs, &output_right_node_contribs,
                     &output_split_types, previous_node_id, min_node_weight, l1,
                     l2, num_buckets);
        f_map.clear();
      }
      previous_node_id = node_id;
      DCHECK_LE(node_id_first, node_id);
      DCHECK_LT(node_id, node_id_last);
      const int32_t feature_dim = stats_summary_indices(idx, 1);
      const int32_t bucket_id = stats_summary_indices(idx, 2);
      const int32_t stat_dim = stats_summary_indices(idx, 3);
      std::pair<FeatureMapIterator, bool> const& f_insert_result = f_map.insert(
          FeatureMapIterator::value_type(feature_dim, BucketMap()));
      auto& b_map = f_insert_result.first->second;
      std::pair<BucketMapIterator, bool> const& b_insert_result =
          b_map.insert(BucketMapIterator::value_type(
              bucket_id, std::vector<float>(stats_dims)));
      auto& stats = b_insert_result.first->second;
      stats[stat_dim] = stats_summary_values(idx);
    }  // for node_id
    // process the last node id
    process_node(f_map, &output_node_ids, &output_gains,
                 &output_feature_dimensions, &output_thresholds,
                 &output_left_node_contribs, &output_right_node_contribs,
                 &output_split_types, previous_node_id, min_node_weight, l1, l2,
                 num_buckets);

    const int num_nodes = output_node_ids.size();
    // output_node_ids
    Tensor* output_node_ids_t = nullptr;
    OP_REQUIRES_OK(context, context->allocate_output("node_ids", {num_nodes},
                                                     &output_node_ids_t));
    auto output_node_ids_vec = output_node_ids_t->vec<int32>();

    // output_gains
    Tensor* output_gains_t;
    OP_REQUIRES_OK(context, context->allocate_output("gains", {num_nodes},
                                                     &output_gains_t));
    auto output_gains_vec = output_gains_t->vec<float>();

    // output_feature_dimensions
    Tensor* output_feature_dimension_t;
    OP_REQUIRES_OK(context,
                   context->allocate_output("feature_dimensions", {num_nodes},
                                            &output_feature_dimension_t));
    auto output_feature_dimensions_vec =
        output_feature_dimension_t->vec<int32>();

    // output_thresholds
    Tensor* output_thresholds_t;
    OP_REQUIRES_OK(context, context->allocate_output("thresholds", {num_nodes},
                                                     &output_thresholds_t));
    auto output_thresholds_vec = output_thresholds_t->vec<int32>();

    // output_left_node_contribs
    Tensor* output_left_node_contribs_t;
    OP_REQUIRES_OK(
        context, context->allocate_output("left_node_contribs", {num_nodes, 1},
                                          &output_left_node_contribs_t));
    auto output_left_node_contribs_matrix =
        output_left_node_contribs_t->matrix<float>();

    // output_right_node_contribs
    Tensor* output_right_node_contribs_t;
    OP_REQUIRES_OK(
        context, context->allocate_output("right_node_contribs", {num_nodes, 1},
                                          &output_right_node_contribs_t));
    auto output_right_node_contribs_matrix =
        output_right_node_contribs_t->matrix<float>();

    // split type
    Tensor* output_split_types_t;
    OP_REQUIRES_OK(
        context, context->allocate_output("split_with_default_directions",
                                          {num_nodes}, &output_split_types_t));
    auto output_split_types_vec = output_split_types_t->vec<tstring>();

    // Sets output tensors from vectors.
    for (int i = 0; i < num_nodes; ++i) {
      output_node_ids_vec(i) = output_node_ids[i];
      // Adjust the gains to penalize by tree complexity.
      output_gains_vec(i) = output_gains[i] - tree_complexity;
      output_feature_dimensions_vec(i) = output_feature_dimensions[i];
      output_thresholds_vec(i) = output_thresholds[i];
      // TODO(crawles): change this for multi-class.
      output_left_node_contribs_matrix(i, 0) = output_left_node_contribs[i];
      output_right_node_contribs_matrix(i, 0) = output_right_node_contribs[i];
      output_split_types_vec(i) = output_split_types[i];
    }
  }

TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to `BoostedTreesSparseCalculateBestFeatureSplit`. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/stats_ops.cc) needs to validate that each value in `stats_summary_indices` is in range. We have patched the issue in GitHub commit e84c975313e8e8e38bb2ea118196369c45c51378. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.


Fixed type confusion bug while resolving promises.

Previously, the internal function njs_promise_perform_then() which
implements PerformPromiseThen() expects its first argument to always be
a promise instance.  This assertion might be invalid because the
functions corresponding to Promise.prototype.then() and
Promise.resolve() incorrectly verified their arguments.

Specifically, the functions recognized their first argument as promise
if it was an object which was an Promise or had Promise object in its
prototype chain.  The later condition is not correct because internal
slots are not inherited according to the spec.

This closes #447 issue in Github.

njs_promise_perform_then(njs_vm_t *vm, njs_value_t *value,
    njs_value_t *fulfilled, njs_value_t *rejected,
    njs_promise_capability_t *capability)
{
    njs_int_t               ret;
    njs_value_t             arguments[2];
    njs_promise_t           *promise;
    njs_function_t          *function;
    njs_promise_data_t      *data;
    njs_promise_reaction_t  *fulfilled_reaction, *rejected_reaction;

    if (!njs_is_function(fulfilled)) {
        fulfilled = njs_value_arg(&njs_value_undefined);
    }

    if (!njs_is_function(rejected)) {
        rejected = njs_value_arg(&njs_value_undefined);
    }

    promise = njs_promise(value);
    data = njs_data(&promise->value);

    fulfilled_reaction = njs_mp_alloc(vm->mem_pool,
                                      sizeof(njs_promise_reaction_t));
    if (njs_slow_path(fulfilled_reaction == NULL)) {
        njs_memory_error(vm);
        return NJS_ERROR;
    }

    fulfilled_reaction->capability = capability;
    fulfilled_reaction->handler = *fulfilled;
    fulfilled_reaction->type = NJS_PROMISE_FULFILL;

    rejected_reaction = njs_mp_alloc(vm->mem_pool,
                                     sizeof(njs_promise_reaction_t));
    if (njs_slow_path(rejected_reaction == NULL)) {
        njs_memory_error(vm);
        return NJS_ERROR;
    }

    rejected_reaction->capability = capability;
    rejected_reaction->handler = *rejected;
    rejected_reaction->type = NJS_PROMISE_REJECTED;

    if (data->state == NJS_PROMISE_PENDING) {
        njs_queue_insert_tail(&data->fulfill_queue, &fulfilled_reaction->link);
        njs_queue_insert_tail(&data->reject_queue, &rejected_reaction->link);

    } else {
        function = njs_promise_create_function(vm,
                                               sizeof(njs_promise_context_t));
        function->u.native = njs_promise_reaction_job;

        if (data->state == NJS_PROMISE_REJECTED) {
            njs_set_data(&arguments[0], rejected_reaction, 0);

            ret = njs_promise_host_rejection_tracker(vm, promise,
                                                     NJS_PROMISE_HANDLE);
            if (njs_slow_path(ret != NJS_OK)) {
                return ret;
            }

        } else {
            njs_set_data(&arguments[0], fulfilled_reaction, 0);
        }

        arguments[1] = data->result;

        ret = njs_promise_add_event(vm, function, arguments, 2);
        if (njs_slow_path(ret != NJS_OK)) {
            return ret;
        }
    }

    data->is_handled = 1;

    if (capability == NULL) {
        njs_vm_retval_set(vm, &njs_value_undefined);

    } else {
        njs_vm_retval_set(vm, &capability->promise);
    }

    return NJS_OK;
}

njs through 0.7.1, used in NGINX, was discovered to contain a control flow hijack caused by a Type Confusion vulnerability in njs_promise_perform_then().


Fixed Array.prototype.lastIndexOf() with unicode string as "this".

Previously, when lastIndexOf() was called with unicode string as "this"
argument and a negative "fromIndex" argument null-pointer dererence
might occur because njs_string_offset() was called with invalid index
value whereas njs_string_offset() should always be called with valid
index argument.

The fix is to verify that from index is valid.

This closes #482 issue on Github.

njs_object_iterate_reverse(njs_vm_t *vm, njs_iterator_args_t *args,
    njs_iterator_handler_t handler)
{
    double              idx;
    int64_t             i, from, to, length;
    njs_int_t           ret;
    njs_array_t         *array, *keys;
    njs_value_t         *entry, *value, prop, character, string_obj;
    const u_char        *p, *end, *pos;
    njs_string_prop_t   string_prop;
    njs_object_value_t  *object;

    value = args->value;
    from = args->from;
    to = args->to;

    if (njs_is_array(value)) {
        array = njs_array(value);

        from += 1;

        while (from-- > to) {
            if (njs_slow_path(!array->object.fast_array)) {
                goto process_object;
            }

            if (njs_fast_path(from < array->length
                              && njs_is_valid(&array->start[from])))
            {
                ret = handler(vm, args, &array->start[from], from);

            } else {
                entry = njs_value_arg(&njs_value_invalid);
                ret = njs_value_property_i64(vm, value, from, &prop);
                if (njs_slow_path(ret != NJS_DECLINED)) {
                    if (ret == NJS_ERROR) {
                        return NJS_ERROR;
                    }

                    entry = ∝
                }

                ret = handler(vm, args, entry, from);
            }

            if (njs_slow_path(ret != NJS_OK)) {
                if (ret == NJS_DONE) {
                    return NJS_DONE;
                }

                return NJS_ERROR;
            }
        }

        return NJS_OK;
    }

    if (njs_is_string(value) || njs_is_object_string(value)) {

        if (njs_is_string(value)) {
            object = njs_object_value_alloc(vm, NJS_OBJ_TYPE_STRING, 0, value);
            if (njs_slow_path(object == NULL)) {
                return NJS_ERROR;
            }

            njs_set_object_value(&string_obj, object);

            args->value = &string_obj;
        }
        else {
            value = njs_object_value(value);
        }

        length = njs_string_prop(&string_prop, value);
        end = string_prop.start + string_prop.size;

        if ((size_t) length == string_prop.size) {
            /* Byte or ASCII string. */

            p = string_prop.start + from;

            i = from + 1;

            while (i-- > to) {
                /* This cannot fail. */
                (void) njs_string_new(vm, &character, p, 1, 1);

                ret = handler(vm, args, &character, i);
                if (njs_slow_path(ret != NJS_OK)) {
                    if (ret == NJS_DONE) {
                        return NJS_DONE;
                    }

                    return NJS_ERROR;
                }

                p--;
            }

        } else {
            /* UTF-8 string. */

            p = njs_string_offset(string_prop.start, end, from);
            p = njs_utf8_next(p, end);

            i = from + 1;

            while (i-- > to) {
                pos = njs_utf8_prev(p);

                /* This cannot fail. */
                (void) njs_string_new(vm, &character, pos, p - pos , 1);

                ret = handler(vm, args, &character, i);
                if (njs_slow_path(ret != NJS_OK)) {
                    if (ret == NJS_DONE) {
                        return NJS_DONE;
                    }

                    return NJS_ERROR;
                }

                p = pos;
            }
        }

        return NJS_OK;
    }

    if (!njs_is_object(value)) {
        return NJS_OK;
    }

process_object:

    if (!njs_fast_object(from - to)) {
        keys = njs_array_indices(vm, value);
        if (njs_slow_path(keys == NULL)) {
            return NJS_ERROR;
        }

        i = keys->length;

        while (i > 0) {
            idx = njs_string_to_index(&keys->start[--i]);

            if (idx < to || idx > from) {
                continue;
            }

            ret = njs_iterator_object_handler(vm, handler, args,
                                              &keys->start[i], idx);
            if (njs_slow_path(ret != NJS_OK)) {
                njs_array_destroy(vm, keys);
                return ret;
            }
        }

        njs_array_destroy(vm, keys);

        return NJS_OK;
    }

    i = from + 1;

    while (i-- > to) {
        ret = njs_iterator_object_handler(vm, handler, args, NULL, i);
        if (njs_slow_path(ret != NJS_OK)) {
            return ret;
        }
    }

    return NJS_OK;
}

Nginx NJS v0.7.2 was discovered to contain a segmentation violation in the function njs_string_offset at src/njs_string.c.


Ensure non-empty rt_nested_splits in tf.raw_ops.RaggedTensorToVariant

PiperOrigin-RevId: 387664237
Change-Id: Ia1700c34b5610873d63561abc86e23b46ead93b3

  void Compute(OpKernelContext* context) override {
    // Read ragged_splits inputs.
    OpInputList ragged_nested_splits_in;
    OP_REQUIRES_OK(context, context->input_list("rt_nested_splits",
                                                &ragged_nested_splits_in));
    const int ragged_nested_splits_len = ragged_nested_splits_in.size();
    RaggedTensorVariant batched_ragged_input;
    // Read ragged_values input.
    batched_ragged_input.set_values(context->input(ragged_nested_splits_len));
    batched_ragged_input.mutable_nested_splits()->reserve(
        ragged_nested_splits_len);
    for (int i = 0; i < ragged_nested_splits_len; i++) {
      batched_ragged_input.append_splits(ragged_nested_splits_in[i]);
    }

    if (!batched_input_) {
      // Encode as a Scalar Variant Tensor.
      Tensor* encoded_scalar;
      OP_REQUIRES_OK(context, context->allocate_output(0, TensorShape({}),
                                                       &encoded_scalar));
      encoded_scalar->scalar<Variant>()() = std::move(batched_ragged_input);
      return;
    }

    // Unbatch the Ragged Tensor and encode the components.
    std::vector<RaggedTensorVariant> unbatched_ragged_input;
    auto batched_splits_top_vec =
        batched_ragged_input.splits(0).vec<SPLIT_TYPE>();
    int num_components = batched_splits_top_vec.size() - 1;
    OP_REQUIRES(context, num_components >= 0,
                errors::Internal("Invalid split argument."));
    OP_REQUIRES_OK(context, UnbatchRaggedZerothDim<VALUE_TYPE, SPLIT_TYPE>(
                                batched_ragged_input, &unbatched_ragged_input));

    // Bundle the encoded scalar Variant Tensors into a rank-1 Variant Tensor.
    Tensor* encoded_vector;
    int output_size = unbatched_ragged_input.size();
    OP_REQUIRES_OK(context,
                   context->allocate_output(0, TensorShape({output_size}),
                                            &encoded_vector));
    auto encoded_vector_t = encoded_vector->vec<Variant>();
    for (int i = 0; i < output_size; i++) {
      encoded_vector_t(i) = unbatched_ragged_input[i];
    }
  }

TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in `tf.raw_ops.RaggedTensorToVariant`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/ragged_tensor_to_variant_op.cc#L129) has an incomplete validation of the splits values, missing the case when the argument would be empty. We have patched the issue in GitHub commit be7a4de6adfbd303ce08be4332554dff70362612. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.


Make Transpose's shape inference function validate that negative `perm` values are within the tensor's rank.

PiperOrigin-RevId: 403252853
Change-Id: Ia6b31b45b237312668bb31c2c3b3c7bbce2d2610

Status TransposeShapeFn(InferenceContext* c) {
  ShapeHandle input = c->input(0);
  ShapeHandle perm_shape = c->input(1);
  const Tensor* perm = c->input_tensor(1);
  DimensionHandle perm_elems = c->NumElements(perm_shape);
  // If we don't have rank information on the input or value information on
  // perm we can't return any shape information, otherwise we have enough
  // information to at least find the rank of the output.
  if (!c->RankKnown(input) && !c->ValueKnown(perm_elems) && perm == nullptr) {
    c->set_output(0, c->UnknownShape());
    return Status::OK();
  }

  // Find our value of the rank.
  int64_t rank;
  if (c->RankKnown(input)) {
    rank = c->Rank(input);
  } else if (c->ValueKnown(perm_elems)) {
    rank = c->Value(perm_elems);
  } else {
    rank = perm->NumElements();
  }
  if (!c->RankKnown(input) && rank < 2) {
    // A permutation array containing a single element is ambiguous. It could
    // indicate either a scalar or a 1-dimensional array, both of which the
    // transpose op returns unchanged.
    c->set_output(0, input);
    return Status::OK();
  }

  std::vector<DimensionHandle> dims;
  dims.resize(rank);
  TF_RETURN_IF_ERROR(c->WithRank(input, rank, &input));
  // Ensure that perm is a vector and has rank elements.
  TF_RETURN_IF_ERROR(c->WithRank(perm_shape, 1, &perm_shape));
  TF_RETURN_IF_ERROR(c->WithValue(perm_elems, rank, &perm_elems));

  // If we know the rank of the input and the value of perm, we can return
  // all shape information, otherwise we can only return rank information,
  // but no information for the dimensions.
  if (perm != nullptr) {
    std::vector<int64_t> data;
    if (perm->dtype() == DT_INT32) {
      data = AsInt64<int32>(perm, rank);
    } else {
      data = AsInt64<int64_t>(perm, rank);
    }

    for (int32_t i = 0; i < rank; ++i) {
      int64_t in_idx = data[i];
      if (in_idx >= rank) {
        return errors::InvalidArgument("perm dim ", in_idx,
                                       " is out of range of input rank ", rank);
      }
      dims[i] = c->Dim(input, in_idx);
    }
  } else {
    for (int i = 0; i < rank; ++i) {
      dims[i] = c->UnknownDim();
    }
  }

  c->set_output(0, c->MakeShape(dims));
  return Status::OK();
}

TensorFlow is an open source platform for machine learning. In affected versions the shape inference function for `Transpose` is vulnerable to a heap buffer overflow. This occurs whenever `perm` contains negative elements. The shape inference function does not validate that the indices in `perm` are all valid. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.


Prevent heap OOB read in TFLite's `gather_nd.cc`.

Passing negative indices is illegal but there was a missing check so that resulted in OOB accesses.

PiperOrigin-RevId: 387208551
Change-Id: I6b7a8a62d3e7c13a16d81619e5bc23ae2cdbc7fd

TfLiteStatus EvalGatherNd(TfLiteContext* context, const TfLiteTensor* params,
                          const TfLiteTensor* indices, TfLiteTensor* output) {
  switch (params->type) {
    case kTfLiteFloat32:
      return GatherNd<float, IndicesT>(params, indices, output);
    case kTfLiteUInt8:
      return GatherNd<uint8_t, IndicesT>(params, indices, output);
    case kTfLiteInt8:
      return GatherNd<int8_t, IndicesT>(params, indices, output);
    case kTfLiteInt16:
      return GatherNd<int16_t, IndicesT>(params, indices, output);
    case kTfLiteInt32:
      return GatherNd<int32_t, IndicesT>(params, indices, output);
    case kTfLiteInt64:
      return GatherNd<int64_t, IndicesT>(params, indices, output);
    case kTfLiteString:
      return GatherNdString<IndicesT>(params, indices, output);
    default:
      context->ReportError(context,
                           "Params type '%s' are not supported by gather_nd.",
                           TfLiteTypeGetName(params->type));
      return kTfLiteError;
  }
}

TensorFlow is an end-to-end open source platform for machine learning. In affected versions TFLite's [`GatherNd` implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/gather_nd.cc#L124) does not support negative indices but there are no checks for this situation. Hence, an attacker can read arbitrary data from the heap by carefully crafting a model with negative values in `indices`. Similar issue exists in [`Gather` implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/gather.cc). We have patched the issue in GitHub commits bb6a0383ed553c286f87ca88c207f6774d5c4a8f and eb921122119a6b6e470ee98b89e65d721663179d. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.


ui/vnc: limit client_cut_text msg payload size

currently a malicious client could define a payload
size of 2^32 - 1 bytes and send up to that size of
data to the vnc server. The server would allocated
that amount of memory which could easily create an
out of memory condition.

This patch limits the payload size to 1MB max.

Please note that client_cut_text messages are currently
silently ignored.

Signed-off-by: Peter Lieven <pl@kamp.de>
Signed-off-by: Gerd Hoffmann <kraxel@redhat.com>

static int protocol_client_msg(VncState *vs, uint8_t *data, size_t len)
{
    int i;
    uint16_t limit;
    VncDisplay *vd = vs->vd;

    if (data[0] > 3) {
        update_displaychangelistener(&vd->dcl, VNC_REFRESH_INTERVAL_BASE);
    }

    switch (data[0]) {
    case VNC_MSG_CLIENT_SET_PIXEL_FORMAT:
        if (len == 1)
            return 20;

        set_pixel_format(vs, read_u8(data, 4), read_u8(data, 5),
                         read_u8(data, 6), read_u8(data, 7),
                         read_u16(data, 8), read_u16(data, 10),
                         read_u16(data, 12), read_u8(data, 14),
                         read_u8(data, 15), read_u8(data, 16));
        break;
    case VNC_MSG_CLIENT_SET_ENCODINGS:
        if (len == 1)
            return 4;

        if (len == 4) {
            limit = read_u16(data, 2);
            if (limit > 0)
                return 4 + (limit * 4);
        } else
            limit = read_u16(data, 2);

        for (i = 0; i < limit; i++) {
            int32_t val = read_s32(data, 4 + (i * 4));
            memcpy(data + 4 + (i * 4), &val, sizeof(val));
        }

        set_encodings(vs, (int32_t *)(data + 4), limit);
        break;
    case VNC_MSG_CLIENT_FRAMEBUFFER_UPDATE_REQUEST:
        if (len == 1)
            return 10;

        framebuffer_update_request(vs,
                                   read_u8(data, 1), read_u16(data, 2), read_u16(data, 4),
                                   read_u16(data, 6), read_u16(data, 8));
        break;
    case VNC_MSG_CLIENT_KEY_EVENT:
        if (len == 1)
            return 8;

        key_event(vs, read_u8(data, 1), read_u32(data, 4));
        break;
    case VNC_MSG_CLIENT_POINTER_EVENT:
        if (len == 1)
            return 6;

        pointer_event(vs, read_u8(data, 1), read_u16(data, 2), read_u16(data, 4));
        break;
    case VNC_MSG_CLIENT_CUT_TEXT:
        if (len == 1)
            return 8;

        if (len == 8) {
            uint32_t dlen = read_u32(data, 4);
            if (dlen > 0)
                return 8 + dlen;
        }

        client_cut_text(vs, read_u32(data, 4), data + 8);
        break;
    case VNC_MSG_CLIENT_QEMU:
        if (len == 1)
            return 2;

        switch (read_u8(data, 1)) {
        case VNC_MSG_CLIENT_QEMU_EXT_KEY_EVENT:
            if (len == 2)
                return 12;

            ext_key_event(vs, read_u16(data, 2),
                          read_u32(data, 4), read_u32(data, 8));
            break;
        case VNC_MSG_CLIENT_QEMU_AUDIO:
            if (len == 2)
                return 4;

            switch (read_u16 (data, 2)) {
            case VNC_MSG_CLIENT_QEMU_AUDIO_ENABLE:
                audio_add(vs);
                break;
            case VNC_MSG_CLIENT_QEMU_AUDIO_DISABLE:
                audio_del(vs);
                break;
            case VNC_MSG_CLIENT_QEMU_AUDIO_SET_FORMAT:
                if (len == 4)
                    return 10;
                switch (read_u8(data, 4)) {
                case 0: vs->as.fmt = AUD_FMT_U8; break;
                case 1: vs->as.fmt = AUD_FMT_S8; break;
                case 2: vs->as.fmt = AUD_FMT_U16; break;
                case 3: vs->as.fmt = AUD_FMT_S16; break;
                case 4: vs->as.fmt = AUD_FMT_U32; break;
                case 5: vs->as.fmt = AUD_FMT_S32; break;
                default:
                    printf("Invalid audio format %d\n", read_u8(data, 4));
                    vnc_client_error(vs);
                    break;
                }
                vs->as.nchannels = read_u8(data, 5);
                if (vs->as.nchannels != 1 && vs->as.nchannels != 2) {
                    printf("Invalid audio channel coount %d\n",
                           read_u8(data, 5));
                    vnc_client_error(vs);
                    break;
                }
                vs->as.freq = read_u32(data, 6);
                break;
            default:
                printf ("Invalid audio message %d\n", read_u8(data, 4));
                vnc_client_error(vs);
                break;
            }
            break;

        default:
            printf("Msg: %d\n", read_u16(data, 0));
            vnc_client_error(vs);
            break;
        }
        break;
    default:
        printf("Msg: %d\n", data[0]);
        vnc_client_error(vs);
        break;
    }

    vnc_read_when(vs, protocol_client_msg, 1);
    return 0;
}

Integer overflow in the VNC display driver in QEMU before 2.1.0 allows attachers to cause a denial of service (process crash) via a CLIENT_CUT_TEXT message, which triggers an infinite loop.


Avoid buffer overflow when loading tensors with insufficient data from checkpoints.

`CopyDataFromTensorSliceToTensorSlice` does not (and cannot conveniently)
provide any bounds checking on its own, so the size is instead checked prior
to passing unvalidated data to that function.

PiperOrigin-RevId: 392971286
Change-Id: If2073b36d4d5eedd386329f56729395fd7effee1

bool TensorSliceReader::CopySliceData(const string& name,
                                      const TensorSlice& slice, T* data) const {
  std::vector<std::pair<TensorSlice, string>> details;
  const TensorSliceSet* tss;
  {
    mutex_lock l(mu_);
    tss = FindTensorSlice(name, slice, &details);
    if (!tss && !all_shards_loaded_) {
      VLOG(1) << "Did not find slice in preferred shard, loading all shards."
              << name << ": " << slice.DebugString();
      LoadAllShards();
      tss = FindTensorSlice(name, slice, &details);
    }
    if (!tss) {
      // No such tensor
      return false;
    }
  }
  // We have the data -- copy it over.
  string value;
  for (const auto& x : details) {
    const TensorSlice& slice_s = x.first;
    const string& fname = x.second;
    int idx = gtl::FindWithDefault(fname_to_index_, fname, -1);
    CHECK_GE(idx, 0) << "Failed to find the index for filename " << fname;
    // We read a record in the corresponding sstable
    const string key = EncodeTensorNameSlice(name, slice_s);
    if (!sss_[idx]->Get(key, &value)) {
      VLOG(1) << "Failed to seek to the record for tensor " << name
              << ", slice " << slice_s.DebugString()
              << ": computed key = " << key;
      return false;
    }
    SavedTensorSlices sts;
    if (!ParseProtoUnlimited(&sts, value)) {
      VLOG(1) << "Failed to parse the record for tensor " << name << ", slice "
              << slice_s.DebugString() << ": computed key = " << key;
      return false;
    }
    CopyDataFromTensorSliceToTensorSlice(
        tss->shape(), slice_s, slice,
        checkpoint::TensorProtoData<T>(sts.data().data()), data);
  }
  return true;
}


vm.c: packed arguments length may be zero for `send` method.

mrb_f_send(mrb_state *mrb, mrb_value self)
{
  mrb_sym name;
  mrb_value block, *regs;
  mrb_method_t m;
  struct RClass *c;
  mrb_callinfo *ci = mrb->c->ci;
  int n = ci->n;

  if (ci->cci > CINFO_NONE) {
  funcall:;
    const mrb_value *argv;
    mrb_int argc;
    mrb_get_args(mrb, "n*&", &name, &argv, &argc, &block);
    return mrb_funcall_with_block(mrb, self, name, argc, argv, block);
  }

  regs = mrb->c->ci->stack+1;

  if (n == 0) {
    mrb_argnum_error(mrb, 0, 1, -1);
  }
  else if (n == 15) {
    name = mrb_obj_to_sym(mrb, RARRAY_PTR(regs[0])[0]);
  }
  else {
    name = mrb_obj_to_sym(mrb, regs[0]);
  }

  c = mrb_class(mrb, self);
  m = mrb_method_search_vm(mrb, &c, name);
  if (MRB_METHOD_UNDEF_P(m)) {            /* call method_mising */
    goto funcall;
  }

  ci->mid = name;
  ci->u.target_class = c;
  /* remove first symbol from arguments */
  if (n == 15) {     /* variable length arguments */
    regs[0] = mrb_ary_subseq(mrb, regs[0], 1, RARRAY_LEN(regs[0]) - 1);
  }
  else { /* n > 0 */
    for (int i=0; i<n; i++) {
      regs[i] = regs[i+1];
    }
    regs[n] = regs[n+1];        /* copy kdict or block */
    if (ci->nk > 0) {
      regs[n+1] = regs[n+2];    /* copy block */
    }
    ci->n--;
  }

  if (MRB_METHOD_CFUNC_P(m)) {
    if (MRB_METHOD_NOARG_P(m)) {
      check_method_noarg(mrb, ci);
    }

    if (MRB_METHOD_PROC_P(m)) {
      mrb_vm_ci_proc_set(ci, MRB_METHOD_PROC(m));
    }
    return MRB_METHOD_CFUNC(m)(mrb, self);
  }
  return exec_irep(mrb, self, MRB_METHOD_PROC(m));
}

Heap-based Buffer Overflow in Homebrew mruby prior to 3.2.


static GF_Err BM_ParseGlobalQuantizer(GF_BifsDecoder *codec, GF_BitStream *bs, GF_List *com_list)
{
	GF_Node *node;
	GF_Command *com;
	GF_CommandField *inf;
	node = gf_bifs_dec_node(codec, bs, NDT_SFWorldNode);
	if (!node) return GF_NON_COMPLIANT_BITSTREAM;

	/*reset global QP*/
	if (codec->scenegraph->global_qp) {
		gf_node_unregister(codec->scenegraph->global_qp, NULL);
	}
	codec->ActiveQP = NULL;
	codec->scenegraph->global_qp = NULL;

	if (gf_node_get_tag(node) != TAG_MPEG4_QuantizationParameter) {
		gf_node_unregister(node, NULL);
		return GF_NON_COMPLIANT_BITSTREAM;
	}

	/*register global QP*/
	codec->ActiveQP = (M_QuantizationParameter *) node;
	codec->ActiveQP->isLocal = 0;
	codec->scenegraph->global_qp = node;

	/*register TWICE: once for the command, and for the scenegraph globalQP*/
	node->sgprivate->num_instances = 2;

	com = gf_sg_command_new(codec->current_graph, GF_SG_GLOBAL_QUANTIZER);
	inf = gf_sg_command_field_new(com);
	inf->new_node = node;
	inf->field_ptr = &inf->new_node;
	inf->fieldType = GF_SG_VRML_SFNODE;
	gf_list_add(com_list, com);
	return GF_OK;
}

Use After Free in GitHub repository gpac/gpac prior to v2.1.0-DEV.


Don't constant-fold DT_RESOURCE constants.

PiperOrigin-RevId: 391803952
Change-Id: I0ea3ec31d3e7dfda0f03b4027a237f08d00a3091

bool IsConstantFoldable(
    const Node* n,
    const std::unordered_map<string, std::vector<PartialTensorShape>>*
        shape_map,
    const std::function<bool(const Node*)>& consider,
    int64_t max_constant_size_in_bytes,
    std::unordered_map<const Node*, std::vector<Tensor>>*
        shape_replacement_map) {
  if (n->IsConstant()) {
    return true;
  }
  if (MaybeReplaceShapeOp(n, shape_map, shape_replacement_map)) {
    return true;
  }
  if (n->op_def().is_stateful()) {
    return false;
  }
  if (consider && !consider(n)) {
    return false;
  }
  if (shape_map != nullptr) {
    // We can skip the node if an output is known to be oversized.
    auto shape_it = shape_map->find(n->name());
    if (shape_it != shape_map->end()) {
      for (int64_t i = 0; i < shape_it->second.size(); ++i) {
        const auto& out_shape = shape_it->second[i];
        if (out_shape.IsFullyDefined() &&
            out_shape.num_elements() * DataTypeSize(n->output_type(i)) >
                max_constant_size_in_bytes) {
          return false;
        }
      }
    }
  }
  if (n->IsControlFlow() || n->IsSend() || n->IsRecv()) {
    return false;
  }
  // TODO(yuanbyu): For now disable these session handle operations.
  if (n->IsGetSessionHandle() || n->IsGetSessionTensor() ||
      n->IsDeleteSessionTensor()) {
    return false;
  }
  if (n->IsSource()) {
    return false;
  }
  if (n->IsSink()) {
    return false;
  }
  if (n->IsFakeParam()) {
    return false;
  }
  // Since constant-folding runs on the CPU, do not attempt to constant-fold
  // operators that have no CPU kernel. Also implies that we will not
  // constant-fold functions.
  // TODO(phawkins): allow constant-folding for functions; functions may
  // be arbitrarily expensive to execute.
  if (!KernelDefAvailable(DeviceType(DEVICE_CPU), n->def())) {
    return false;
  }
  // Do not constant fold nodes which will be allocated by ScopedAllocator.
  // This is because the constant-folding graph will not contain the
  // `_ScopedAllocator` node, and that is necessary to be able to run a node
  // that will use this allocator.
  if (n->attrs().Find(kScopedAllocatorAttrName) != nullptr) {
    VLOG(2) << "Skip node [" << n->DebugString()
            << "] for constant folding due to scoped allocator";
    return false;
  }
  return true;
}

TensorFlow is an open source platform for machine learning. In affected versions during TensorFlow's Grappler optimizer phase, constant folding might attempt to deep copy a resource tensor. This results in a segfault, as these tensors are supposed to not change. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.


Prevent heap OOB read in TFLite's `gather.cc`.

Passing negative indices is illegal but there was a missing check so that resulted in OOB accesses.

PiperOrigin-RevId: 387231300
Change-Id: I3111b54b2f232638d795be17efc46abe4ede6bf8

TfLiteStatus Gather(const TfLiteGatherParams& params, const TfLiteTensor* input,
                    const TfLiteTensor* positions, TfLiteTensor* output) {
  tflite::GatherParams op_params;
  op_params.axis = params.axis;
  op_params.batch_dims = params.batch_dims;
  optimized_ops::Gather(op_params, GetTensorShape(input),
                        GetTensorData<InputT>(input), GetTensorShape(positions),
                        GetTensorData<PositionsT>(positions),
                        GetTensorShape(output), GetTensorData<InputT>(output));
  return kTfLiteOk;
}


Validate axis input in tf.raw_ops.QuantizeAndDequantizeV4Grad

PiperOrigin-RevId: 388291385
Change-Id: I3bab68dc61d935afa96c0da021a7b722c6dc8dc8

  void Compute(OpKernelContext* ctx) override {
    const Tensor& gradient = ctx->input(0);
    const Tensor& input = ctx->input(1);
    Tensor* input_backprop = nullptr;
    OP_REQUIRES_OK(ctx,
                   ctx->allocate_output(0, input.shape(), &input_backprop));

    OP_REQUIRES(
        ctx, input.IsSameSize(gradient),
        errors::InvalidArgument("gradient and input must be the same size"));
    const int depth = (axis_ == -1) ? 1 : input.dim_size(axis_);
    const Tensor& input_min_tensor = ctx->input(2);
    OP_REQUIRES(ctx,
                input_min_tensor.dims() == 0 || input_min_tensor.dims() == 1,
                errors::InvalidArgument(
                    "Input min tensor must have dimension 1. Recieved ",
                    input_min_tensor.dims(), "."));
    const Tensor& input_max_tensor = ctx->input(3);
    OP_REQUIRES(ctx,
                input_max_tensor.dims() == 0 || input_max_tensor.dims() == 1,
                errors::InvalidArgument(
                    "Input max tensor must have dimension 1. Recieved ",
                    input_max_tensor.dims(), "."));
    if (axis_ != -1) {
      OP_REQUIRES(
          ctx, input_min_tensor.dim_size(0) == depth,
          errors::InvalidArgument("min has incorrect size, expected ", depth,
                                  " was ", input_min_tensor.dim_size(0)));
      OP_REQUIRES(
          ctx, input_max_tensor.dim_size(0) == depth,
          errors::InvalidArgument("max has incorrect size, expected ", depth,
                                  " was ", input_max_tensor.dim_size(0)));
    }

    TensorShape min_max_shape(input_min_tensor.shape());
    Tensor* input_min_backprop;
    OP_REQUIRES_OK(ctx,
                   ctx->allocate_output(1, min_max_shape, &input_min_backprop));

    Tensor* input_max_backprop;
    OP_REQUIRES_OK(ctx,
                   ctx->allocate_output(2, min_max_shape, &input_max_backprop));

    if (axis_ == -1) {
      functor::QuantizeAndDequantizeOneScaleGradientFunctor<Device, T> f;
      f(ctx->eigen_device<Device>(), gradient.template flat<T>(),
        input.template flat<T>(), input_min_tensor.scalar<T>(),
        input_max_tensor.scalar<T>(), input_backprop->template flat<T>(),
        input_min_backprop->template scalar<T>(),
        input_max_backprop->template scalar<T>());
    } else {
      functor::QuantizeAndDequantizePerChannelGradientFunctor<Device, T> f;
      f(ctx->eigen_device<Device>(),
        gradient.template flat_inner_outer_dims<T, 3>(axis_ - 1),
        input.template flat_inner_outer_dims<T, 3>(axis_ - 1),
        &input_min_tensor, &input_max_tensor,
        input_backprop->template flat_inner_outer_dims<T, 3>(axis_ - 1),
        input_min_backprop->template flat<T>(),
        input_max_backprop->template flat<T>());
    }
  }

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of `tf.raw_ops.QuantizeAndDequantizeV4Grad` is vulnerable to an integer overflow issue caused by converting a signed integer value to an unsigned one and then allocating memory based on this value. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L126) uses the `axis` value as the size argument to `absl::InlinedVector` constructor. But, the constructor uses an unsigned type for the argument, so the implicit conversion transforms the negative value to a large integer. We have patched the issue in GitHub commit 96f364a1ca3009f98980021c4b32be5fdcca33a1. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, and TensorFlow 2.4.3, as these are also affected and still in supported range.


exec: --cap do not set inheritable capabilities

Closes: CVE-2022-27650

Signed-off-by: Giuseppe Scrivano <gscrivan@redhat.com>

crun_command_exec (struct crun_global_arguments *global_args, int argc, char **argv, libcrun_error_t *err)
{
  int first_arg = 0, ret = 0;
  libcrun_context_t crun_context = {
    0,
  };
  cleanup_process_schema runtime_spec_schema_config_schema_process *process = NULL;
  struct libcrun_container_exec_options_s exec_opts;

  memset (&exec_opts, 0, sizeof (exec_opts));
  exec_opts.struct_size = sizeof (exec_opts);

  crun_context.preserve_fds = 0;
  crun_context.listen_fds = 0;

  argp_parse (&run_argp, argc, argv, ARGP_IN_ORDER, &first_arg, &exec_options);
  crun_assert_n_args (argc - first_arg, exec_options.process ? 1 : 2, -1);

  ret = init_libcrun_context (&crun_context, argv[first_arg], global_args, err);
  if (UNLIKELY (ret < 0))
    return ret;

  crun_context.detach = exec_options.detach;
  crun_context.console_socket = exec_options.console_socket;
  crun_context.pid_file = exec_options.pid_file;
  crun_context.preserve_fds = exec_options.preserve_fds;

  if (getenv ("LISTEN_FDS"))
    {
      crun_context.listen_fds = strtoll (getenv ("LISTEN_FDS"), NULL, 10);
      crun_context.preserve_fds += crun_context.listen_fds;
    }

  if (exec_options.process)
    exec_opts.path = exec_options.process;
  else
    {
      process = xmalloc0 (sizeof (*process));
      int i;

      process->args_len = argc;
      process->args = xmalloc0 ((argc + 1) * sizeof (*process->args));
      for (i = 0; i < argc - first_arg; i++)
        process->args[i] = xstrdup (argv[first_arg + i + 1]);
      process->args[i] = NULL;
      if (exec_options.cwd)
        process->cwd = exec_options.cwd;
      process->terminal = exec_options.tty;
      process->env = exec_options.env;
      process->env_len = exec_options.env_size;
      process->user = make_oci_process_user (exec_options.user);

      if (exec_options.process_label != NULL)
        process->selinux_label = exec_options.process_label;

      if (exec_options.apparmor != NULL)
        process->apparmor_profile = exec_options.apparmor;

      if (exec_options.cap_size > 0)
        {
          runtime_spec_schema_config_schema_process_capabilities *capabilities
              = xmalloc (sizeof (runtime_spec_schema_config_schema_process_capabilities));

          capabilities->effective = exec_options.cap;
          capabilities->effective_len = exec_options.cap_size;

          capabilities->inheritable = dup_array (exec_options.cap, exec_options.cap_size);
          capabilities->inheritable_len = exec_options.cap_size;

          capabilities->bounding = dup_array (exec_options.cap, exec_options.cap_size);
          capabilities->bounding_len = exec_options.cap_size;

          capabilities->ambient = dup_array (exec_options.cap, exec_options.cap_size);
          capabilities->ambient_len = exec_options.cap_size;

          capabilities->permitted = dup_array (exec_options.cap, exec_options.cap_size);
          capabilities->permitted_len = exec_options.cap_size;

          process->capabilities = capabilities;
        }

      // noNewPriviledges will remain `false` if basespec has `false` unless specified
      // Default is always `true` in generated basespec config
      if (exec_options.no_new_privs)
        process->no_new_privileges = 1;

      exec_opts.process = process;
    }

  exec_opts.cgroup = exec_options.cgroup;

  return libcrun_container_exec_with_options (&crun_context, argv[first_arg], &exec_opts, err);
}

A flaw was found in crun where containers were incorrectly started with non-empty default permissions. A vulnerability was found in Moby (Docker Engine) where containers were started incorrectly with non-empty inheritable Linux process capabilities. This flaw allows an attacker with access to programs with inheritable file capabilities to elevate those capabilities to the permitted set when execve(2) runs.


Fix overflow CHECK issue with `tf.raw_ops.UnsortedSegmentJoin`.

PiperOrigin-RevId: 370766155
Change-Id: I33e7c6626224e1060a8a4ab51ad5d861c6d4c63e

  void Compute(OpKernelContext* context) override {
    const Tensor& input = context->input(0);
    const TensorShape& input_shape = input.shape();
    const int32 input_dims = input_shape.dims();

    const Tensor& segment_id = context->input(1);
    const TensorShape& segment_id_shape = segment_id.shape();
    const int32 segment_dims = segment_id_shape.dims();

    const Tensor& num_segments_tensor = context->input(2);
    auto num_segments = num_segments_tensor.scalar<NUM_SEGMENTS_TYPE>()();

    OP_REQUIRES(context, segment_dims != 0,
                errors::InvalidArgument("Segment_id cannot have rank 0"));

    OP_REQUIRES(
        context, segment_dims <= input_dims,
        errors::OutOfRange("Invalid segment_id rank ", segment_dims,
                           " for input with ", input_dims, " dimension(s)"));
    for (auto i = 0; i < segment_dims; i++) {
      OP_REQUIRES(
          context, segment_id_shape.dim_size(i) == input_shape.dim_size(i),
          errors::InvalidArgument(
              "Segment dimension is ", segment_id_shape.dim_size(i),
              " while input dimension is ", input_dims, " in rank ", i));
    }

    // Making output tensor.
    Tensor* output_tensor = nullptr;
    TensorShape output_shape =
        GetOutputShape(input_shape, segment_id_shape, num_segments);
    OP_REQUIRES_OK(context, context->allocate_output("output", output_shape,
                                                     &output_tensor));

    // Preparating flat tensors.
    auto output_flat = output_tensor->flat<tstring>();
    auto flat_segment_id = segment_id.flat<INDICES_TYPE>();
    auto flat_input = input.flat<tstring>();

    for (int i = 0; i < flat_segment_id.size(); i++) {
      OP_REQUIRES(
          context,
          ((flat_segment_id(i) < num_segments) && (flat_segment_id(i) >= 0)),
          errors::InvalidArgument(
              "segment_ids are not allowed to exceed num_segments or"
              " to have negative values."));
    }

    int64 big_stride;
    int64 small_stride;
    std::tie(big_stride, small_stride) =
        GetStrides<INDICES_TYPE>(input_shape, segment_id_shape);
    auto relative_offset_set =
        GetFlattenedRelativeOffsets<INDICES_TYPE>(small_stride, big_stride);
    for (auto start_offset = 0; start_offset < big_stride; start_offset++) {
      for (auto i = 0; i < relative_offset_set.size(); i++) {
        auto output_index = start_offset + flat_segment_id(i) * big_stride;
        auto offset = start_offset + relative_offset_set[i];
        if (output_flat(output_index).length() != 0)
          output_flat(output_index).append(separator_.c_str());
        output_flat(output_index).append(flat_input(offset));
      }
    }
  }

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service by controlling the values of `num_segments` tensor argument for `UnsortedSegmentJoin`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a2a607db15c7cd01d754d37e5448d72a13491bdb/tensorflow/core/kernels/unsorted_segment_join_op.cc#L92-L93) assumes that the `num_segments` tensor is a valid scalar. Since the tensor is empty the `CHECK` involved in `.scalar<T>()()` that checks that the number of elements is exactly 1 will be invalidated and this would result in process termination. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.


Fix FPE in inpace update ops.

PiperOrigin-RevId: 388303197
Change-Id: Ib48309b6213ffe53eba81004b00e889d653e4b83

  void Compute(OpKernelContext* ctx) override {
    auto x = ctx->input(0);
    auto i = ctx->input(1);
    auto v = ctx->input(2);

    OP_REQUIRES(ctx, TensorShapeUtils::IsVector(i.shape()),
                errors::InvalidArgument("i must be a vector. ",
                                        i.shape().DebugString()));
    OP_REQUIRES(ctx, x.dims() == v.dims(),
                errors::InvalidArgument(
                    "x and v shape doesn't match (ranks differ): ",
                    x.shape().DebugString(), " vs. ", v.shape().DebugString()));
    for (int i = 1; i < x.dims(); ++i) {
      OP_REQUIRES(
          ctx, x.dim_size(i) == v.dim_size(i),
          errors::InvalidArgument("x and v shape doesn't match at index ", i,
                                  " : ", x.shape().DebugString(), " vs. ",
                                  v.shape().DebugString()));
    }
    OP_REQUIRES(ctx, i.dim_size(0) == v.dim_size(0),
                errors::InvalidArgument(
                    "i and x shape doesn't match at index 0: ",
                    i.shape().DebugString(), " vs. ", v.shape().DebugString()));

    Tensor y = x;  // This creates an alias intentionally.
    // Skip processing if tensors are empty.
    if (x.NumElements() > 0 || v.NumElements() > 0) {
      OP_REQUIRES_OK(ctx, DoCompute(ctx, i, v, &y));
    }
    ctx->set_output(0, y);
  }

TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause a floating point exception by calling inplace operations with crafted arguments that would result in a division by 0. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/inplace_ops.cc#L283) has a logic error: it should skip processing if `x` and `v` are empty but the code uses `||` instead of `&&`. We have patched the issue in GitHub commit e86605c0a336c088b638da02135ea6f9f6753618. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.


Prevent overflow due to integer conversion to unsigned.

PiperOrigin-RevId: 387738045
Change-Id: Id7e95bc07e02df1c66b72bd09f389608c87bdebe

  void Compute(OpKernelContext* context) override {
    // boxes: [batch_size, num_anchors, q, 4]
    const Tensor& boxes = context->input(0);
    // scores: [batch_size, num_anchors, num_classes]
    const Tensor& scores = context->input(1);
    OP_REQUIRES(
        context, (boxes.dim_size(0) == scores.dim_size(0)),
        errors::InvalidArgument("boxes and scores must have same batch size"));

    // max_output_size: scalar
    const Tensor& max_output_size = context->input(2);
    OP_REQUIRES(
        context, TensorShapeUtils::IsScalar(max_output_size.shape()),
        errors::InvalidArgument("max_size_per_class must be 0-D, got shape ",
                                max_output_size.shape().DebugString()));
    const int max_size_per_class = max_output_size.scalar<int>()();
    // max_total_size: scalar
    const Tensor& max_total_size = context->input(3);
    OP_REQUIRES(
        context, TensorShapeUtils::IsScalar(max_total_size.shape()),
        errors::InvalidArgument("max_total_size must be 0-D, got shape ",
                                max_total_size.shape().DebugString()));
    const int max_total_size_per_batch = max_total_size.scalar<int>()();
    OP_REQUIRES(context, max_total_size_per_batch > 0,
                errors::InvalidArgument("max_total_size must be > 0"));
    // Throw warning when `max_total_size` is too large as it may cause OOM.
    if (max_total_size_per_batch > pow(10, 6)) {
      LOG(WARNING) << "Detected a large value for `max_total_size`. This may "
                   << "cause OOM error. (max_total_size: "
                   << max_total_size.scalar<int>()() << ")";
    }
    // iou_threshold: scalar
    const Tensor& iou_threshold = context->input(4);
    OP_REQUIRES(context, TensorShapeUtils::IsScalar(iou_threshold.shape()),
                errors::InvalidArgument("iou_threshold must be 0-D, got shape ",
                                        iou_threshold.shape().DebugString()));
    const float iou_threshold_val = iou_threshold.scalar<float>()();

    // score_threshold: scalar
    const Tensor& score_threshold = context->input(5);
    OP_REQUIRES(
        context, TensorShapeUtils::IsScalar(score_threshold.shape()),
        errors::InvalidArgument("score_threshold must be 0-D, got shape ",
                                score_threshold.shape().DebugString()));
    const float score_threshold_val = score_threshold.scalar<float>()();

    OP_REQUIRES(context, iou_threshold_val >= 0 && iou_threshold_val <= 1,
                errors::InvalidArgument("iou_threshold must be in [0, 1]"));
    int num_boxes = 0;
    const int num_classes = scores.dim_size(2);
    ParseAndCheckCombinedNMSBoxSizes(context, boxes, &num_boxes, num_classes);
    CheckCombinedNMSScoreSizes(context, num_boxes, scores);

    if (!context->status().ok()) {
      return;
    }
    BatchedNonMaxSuppressionOp(context, boxes, scores, num_boxes,
                               max_size_per_class, max_total_size_per_batch,
                               score_threshold_val, iou_threshold_val,
                               pad_per_class_, clip_boxes_);
  }

TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause denial of service in applications serving models using `tf.raw_ops.NonMaxSuppressionV5` by triggering a division by 0. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/image/non_max_suppression_op.cc#L170-L271) uses a user controlled argument to resize a `std::vector`. However, as `std::vector::resize` takes the size argument as a `size_t` and `output_size` is an `int`, there is an implicit conversion to unsigned. If the attacker supplies a negative value, this conversion results in a crash. A similar issue occurs in `CombinedNonMaxSuppression`. We have patched the issue in GitHub commit 3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d and commit [b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.


Fix integer overflow in string search causing oobread ##crash

* Reported by @greatergoodest via huntrdev
* BountyID: 8a3dc5cb-08b3-4807-82b2-77f08c137a04
* Reproducer bfileovf

static int string_scan_range(RList *list, RBinFile *bf, int min,
			      const ut64 from, const ut64 to, int type, int raw, RBinSection *section) {
	RBin *bin = bf->rbin;
	ut8 tmp[R_STRING_SCAN_BUFFER_SIZE];
	ut64 str_start, needle = from;
	int count = 0, i, rc, runes;
	int str_type = R_STRING_TYPE_DETECT;

	// if list is null it means its gonna dump
	r_return_val_if_fail (bf, -1);

	if (type == -1) {
		type = R_STRING_TYPE_DETECT;
	}
	if (from == to) {
		return 0;
	}
	if (from > to) {
		eprintf ("Invalid range to find strings 0x%"PFMT64x" .. 0x%"PFMT64x"\n", from, to);
		return -1;
	}
	st64 len = (st64)(to - from);
	if (len < 1 || len > ST32_MAX) {
		eprintf ("String scan range is invalid (%"PFMT64d" bytes)\n", len);
		return -1;
	}
	ut8 *buf = calloc (len, 1);
	if (!buf || !min) {
		free (buf);
		return -1;
	}
	st64 vdelta = 0, pdelta = 0;
	RBinSection *s = NULL;
	bool ascii_only = false;
	PJ *pj = NULL;
	if (bf->strmode == R_MODE_JSON && !list) {
		pj = pj_new ();
		if (pj) {
			pj_a (pj);
		}
	}
	r_buf_read_at (bf->buf, from, buf, len);
	char *charset = r_sys_getenv ("RABIN2_CHARSET");
	if (!R_STR_ISEMPTY (charset)) {
		RCharset *ch = r_charset_new ();
		if (r_charset_use (ch, charset)) {
			int outlen = len * 4;
			ut8 *out = calloc (len, 4);
			if (out) {
				int res = r_charset_encode_str (ch, out, outlen, buf, len);
				int i;
				// TODO unknown chars should be translated to null bytes
				for (i = 0; i < res; i++) {
					if (out[i] == '?') {
						out[i] = 0;
					}
				}
				len = res;
				free (buf);
				buf = out;
			} else {
				eprintf ("Cannot allocate\n");
			}
		} else {
			eprintf ("Invalid value for RABIN2_CHARSET.\n");
		}
		r_charset_free (ch);
	}
	free (charset);
	RConsIsBreaked is_breaked = (bin && bin->consb.is_breaked)? bin->consb.is_breaked: NULL;
	// may oobread
	while (needle < to) {
		if (is_breaked && is_breaked ()) {
			break;
		}
		// smol optimization
		if (needle + 4 < to) {
			ut32 n1 = r_read_le32 (buf + needle - from);
			if (!n1) {
				needle += 4;
				continue;
			}
		}
		rc = r_utf8_decode (buf + needle - from, to - needle, NULL);
		if (!rc) {
			needle++;
			continue;
		}
		bool addr_aligned = !(needle % 4);

		if (type == R_STRING_TYPE_DETECT) {
			char *w = (char *)buf + needle + rc - from;
			if (((to - needle) > 8 + rc)) {
				// TODO: support le and be
				bool is_wide32le = (needle + rc + 2 < to) && (!w[0] && !w[1] && !w[2] && w[3] && !w[4]);
				// reduce false positives
				if (is_wide32le) {
					if (!w[5] && !w[6] && w[7] && w[8]) {
						is_wide32le = false;
					}
				}
				if (!addr_aligned) {
					is_wide32le = false;
				}
				///is_wide32be &= (n1 < 0xff && n11 < 0xff); // false; // n11 < 0xff;
				if (is_wide32le  && addr_aligned) {
					str_type = R_STRING_TYPE_WIDE32; // asume big endian,is there little endian w32?
				} else {
					// bool is_wide = (n1 && n2 && n1 < 0xff && (!n2 || n2 < 0xff));
					bool is_wide = needle + rc + 4 < to && !w[0] && w[1] && !w[2] && w[3] && !w[4];
					str_type = is_wide? R_STRING_TYPE_WIDE: R_STRING_TYPE_ASCII;
				}
			} else {
				if (rc > 1) {
					str_type = R_STRING_TYPE_UTF8; // could be charset if set :?
				} else {
					str_type = R_STRING_TYPE_ASCII;
				}
			}
		} else if (type == R_STRING_TYPE_UTF8) {
			str_type = R_STRING_TYPE_ASCII; // initial assumption
		} else {
			str_type = type;
		}
		runes = 0;
		str_start = needle;

		/* Eat a whole C string */
		for (i = 0; i < sizeof (tmp) - 4 && needle < to; i += rc) {
			RRune r = {0};
			if (str_type == R_STRING_TYPE_WIDE32) {
				rc = r_utf32le_decode (buf + needle - from, to - needle, &r);
				if (rc) {
					rc = 4;
				}
			} else if (str_type == R_STRING_TYPE_WIDE) {
				rc = r_utf16le_decode (buf + needle - from, to - needle, &r);
				if (rc == 1) {
					rc = 2;
				}
			} else {
				rc = r_utf8_decode (buf + needle - from, to - needle, &r);
				if (rc > 1) {
					str_type = R_STRING_TYPE_UTF8;
				}
			}

			/* Invalid sequence detected */
			if (!rc || (ascii_only && r > 0x7f)) {
				needle++;
				break;
			}

			needle += rc;

			if (r_isprint (r) && r != '\\') {
				if (str_type == R_STRING_TYPE_WIDE32) {
					if (r == 0xff) {
						r = 0;
					}
				}
				rc = r_utf8_encode (tmp + i, r);
				runes++;
				/* Print the escape code */
			} else if (r && r < 0x100 && strchr ("\b\v\f\n\r\t\a\033\\", (char)r)) {
				if ((i + 32) < sizeof (tmp) && r < 93) {
					tmp[i + 0] = '\\';
					tmp[i + 1] = "       abtnvfr             e  "
					             "                              "
					             "                              "
					             "  \\"[r];
				} else {
					// string too long
					break;
				}
				rc = 2;
				runes++;
			} else {
				/* \0 marks the end of C-strings */
				break;
			}
		}

		tmp[i++] = '\0';

		if (runes < min && runes >= 2 && str_type == R_STRING_TYPE_ASCII && needle < to) {
			// back up past the \0 to the last char just in case it starts a wide string
			needle -= 2;
		}
		if (runes >= min) {
			// reduce false positives
			int j, num_blocks, *block_list;
			int *freq_list = NULL, expected_ascii, actual_ascii, num_chars;
			if (str_type == R_STRING_TYPE_ASCII) {
				for (j = 0; j < i; j++) {
					char ch = tmp[j];
					if (ch != '\n' && ch != '\r' && ch != '\t') {
						if (!IS_PRINTABLE (tmp[j])) {
							continue;
						}
					}
				}
			}
			switch (str_type) {
			case R_STRING_TYPE_UTF8:
			case R_STRING_TYPE_WIDE:
			case R_STRING_TYPE_WIDE32:
				num_blocks = 0;
				block_list = r_utf_block_list ((const ut8*)tmp, i - 1,
						str_type == R_STRING_TYPE_WIDE? &freq_list: NULL);
				if (block_list) {
					for (j = 0; block_list[j] != -1; j++) {
						num_blocks++;
					}
				}
				if (freq_list) {
					num_chars = 0;
					actual_ascii = 0;
					for (j = 0; freq_list[j] != -1; j++) {
						num_chars += freq_list[j];
						if (!block_list[j]) { // ASCII
							actual_ascii = freq_list[j];
						}
					}
					free (freq_list);
					expected_ascii = num_blocks ? num_chars / num_blocks : 0;
					if (actual_ascii > expected_ascii) {
						ascii_only = true;
						needle = str_start;
						free (block_list);
						continue;
					}
				}
				free (block_list);
				if (num_blocks > R_STRING_MAX_UNI_BLOCKS) {
					needle++;
					continue;
				}
			}
			RBinString *bs = R_NEW0 (RBinString);
			if (!bs) {
				break;
			}
			bs->type = str_type;
			bs->length = runes;
			bs->size = needle - str_start;
			bs->ordinal = count++;
			// TODO: move into adjust_offset
			switch (str_type) {
			case R_STRING_TYPE_WIDE:
				if (str_start - from > 1) {
					const ut8 *p = buf + str_start - 2 - from;
					if (p[0] == 0xff && p[1] == 0xfe) {
						str_start -= 2; // \xff\xfe
					}
				}
				break;
			case R_STRING_TYPE_WIDE32:
				if (str_start - from > 3) {
					const ut8 *p = buf + str_start - 4 - from;
					if (p[0] == 0xff && p[1] == 0xfe) {
						str_start -= 4; // \xff\xfe\x00\x00
					}
				}
				break;
			}
			if (!s) {
				if (section) {
					s = section;
				} else if (bf->o) {
					s = r_bin_get_section_at (bf->o, str_start, false);
				}
				if (s) {
					vdelta = s->vaddr;
					pdelta = s->paddr;
				}
			}
			ut64 baddr = bf->loadaddr && bf->o? bf->o->baddr: bf->loadaddr;
			bs->paddr = str_start + baddr;
			bs->vaddr = str_start - pdelta + vdelta + baddr;
			bs->string = r_str_ndup ((const char *)tmp, i);
			if (list) {
				r_list_append (list, bs);
				if (bf->o) {
					ht_up_insert (bf->o->strings_db, bs->vaddr, bs);
				}
			} else {
				print_string (bf, bs, raw, pj);
				r_bin_string_free (bs);
			}
			if (from == 0 && to == bf->size) {
				/* force lookup section at the next one */
				s = NULL;
			}
		}
		ascii_only = false;
	}
	free (buf);
	if (pj) {
		pj_end (pj);
		if (bin) {
			RIO *io = bin->iob.io;
			if (io) {
				io->cb_printf ("%s", pj_string (pj));
			}
		}
		pj_free (pj);
	}
	return count;
}

Out-of-bounds Read in GitHub repository radareorg/radare2 prior to 5.7.0.


Fix tf.raw_ops.LoadAndRemapMatrix vulnerability with invalid `row_remapping`.

Check that `row_remapping` has the correct dims().

PiperOrigin-RevId: 445522800

  void Compute(OpKernelContext* context) override {
    // Checks what we're remapping and inverts the relevant remapping Tensors to
    // be maps with key = old ID, value = new ID.
    std::unordered_map<int64_t, int64_t> old_row_to_new_row_map;
    std::vector<bool> row_id_present;
    const Tensor* row_remapping_t;
    OP_REQUIRES_OK(context, context->input("row_remapping", &row_remapping_t));
    const auto row_remapping = row_remapping_t->vec<int64_t>();
    OP_REQUIRES(context, row_remapping.size() == num_rows_,
                errors::InvalidArgument(strings::StrCat(
                    "Size of row_remapping is ", row_remapping.size(),
                    " instead of being equal to num_rows=", num_rows_)));
    OP_REQUIRES_OK(context, RemapVectorToMap(row_remapping, &row_id_present,
                                             &old_row_to_new_row_map));

    // Calculates the min/max old row ID that we need to read, to save us from
    // reading some unnecessary slices of the old tensor.
    int64_t min_old_row = -1;
    int64_t max_old_row = -1;
    for (int i = 0; i < row_remapping.size(); ++i) {
      if (min_old_row < 0 ||
          (row_remapping(i) >= 0 && row_remapping(i) < min_old_row)) {
        min_old_row = row_remapping(i);
      }
      if (max_old_row < 0 ||
          (row_remapping(i) >= 0 && row_remapping(i) > max_old_row)) {
        max_old_row = row_remapping(i);
      }
    }

    // Processes the remapping for columns.
    std::unordered_map<int64_t, int64_t> old_col_to_new_col_map;
    std::vector<bool> col_id_present;
    const Tensor* col_remapping_t;
    OP_REQUIRES_OK(context, context->input("col_remapping", &col_remapping_t));
    const auto col_remapping = col_remapping_t->vec<int64_t>();
    // Note that we always "remap rows", even when the row vocabulary does
    // not change, because partitioning requires a mapping from partitioned
    // Variables to the full checkpoints we load.
    const bool remap_cols = col_remapping.size() > 0;
    if (remap_cols) {
      OP_REQUIRES(
          context, col_remapping.size() == num_cols_,
          errors::InvalidArgument(strings::StrCat(
              "Provided col_remapping, but its size is ", col_remapping.size(),
              " instead of being equal to num_cols=", num_cols_)));
      OP_REQUIRES_OK(context, RemapVectorToMap(col_remapping, &col_id_present,
                                               &old_col_to_new_col_map));
    } else {
      col_id_present.clear();
      col_id_present.resize(num_cols_, true);
    }

    // Processes the checkpoint source and the provided Tensor name.
    const Tensor* ckpt_path_t;
    OP_REQUIRES_OK(context, context->input("ckpt_path", &ckpt_path_t));
    OP_REQUIRES(
        context, ckpt_path_t->NumElements() == 1,
        errors::InvalidArgument("The `ckpt_path` tensor must have exactly one "
                                "element, got tensor of shape ",
                                ckpt_path_t->shape().DebugString()));
    const string& ckpt_path = ckpt_path_t->scalar<tstring>()();
    const Tensor* old_tensor_name_t;
    OP_REQUIRES_OK(context,
                   context->input("old_tensor_name", &old_tensor_name_t));
    const string& old_tensor_name = old_tensor_name_t->scalar<tstring>()();

    LOG(INFO) << "Processing checkpoint : " << ckpt_path;
    BundleReader reader(context->env(), ckpt_path);
    OP_REQUIRES_OK(context, reader.status());

    DataType tensor_type;
    TensorShape tensor_shape;
    OP_REQUIRES_OK(context, reader.LookupDtypeAndShape(
                                old_tensor_name, &tensor_type, &tensor_shape));
    OP_REQUIRES(context, tensor_type == DT_FLOAT,
                errors::InvalidArgument(strings::StrCat(
                    "Tensor ", old_tensor_name, " has invalid type ",
                    DataTypeString(tensor_type), " instead of expected type ",
                    DataTypeString(DT_FLOAT))));
    // This op is limited to loading Tensors of rank 2 (matrices).
    OP_REQUIRES(
        context, tensor_shape.dims() == 2,
        errors::InvalidArgument(strings::StrCat(
            "Tensor ", old_tensor_name, " has shape ",
            tensor_shape.DebugString(), " of invalid rank ",
            tensor_shape.dims(), " instead of expected shape of rank 2.")));

    if (!remap_cols) {
      // TODO(weiho): Consider relaxing this restriction to allow partial column
      // loading (even when no column remapping is specified) if there turns out
      // to be a use case for it.
      OP_REQUIRES(context, num_cols_ == tensor_shape.dim_size(1),
                  errors::InvalidArgument(strings::StrCat(
                      "Tensor ", old_tensor_name, " has shape ",
                      tensor_shape.DebugString(),
                      ", where the size of its 2nd dimension is ",
                      tensor_shape.dim_size(1),
                      " instead of being equal to num_cols=", num_cols_)));
    }

    // Uses TensorSlice to potentially load the old tensor in chunks in case
    // memory usage is a concern.
    std::vector<TensorSlice> tensor_slices;
    TensorSlice slice(tensor_shape.dims());
    if (min_old_row >= 0 && max_old_row >= 0) {
      int64_t row_start = min_old_row;
      // TODO(weiho): Given the list of old row IDs of interest (the keys of
      // old_row_to_new_row_map), we could also try something smarter to
      // find some minimal set of covering ranges for the list of old row IDs
      // such that the size of each range is less than max_rows_in_memory_.
      while (row_start <= max_old_row) {
        const int64_t slice_length =
            max_rows_in_memory_ <= 0
                // If max_rows_in_memory_ <= 0, we just load the entire chunk.
                ? max_old_row - row_start + 1
                : std::min(max_rows_in_memory_, max_old_row - row_start + 1);
        slice.set_start(0, row_start);
        slice.set_length(0, slice_length);
        tensor_slices.push_back(slice);
        row_start += slice_length;
      }
    }

    // Allocates the output matrix.
    Tensor* output_matrix_t = nullptr;
    OP_REQUIRES_OK(context,
                   context->allocate_output("output_matrix",
                                            TensorShape({num_rows_, num_cols_}),
                                            &output_matrix_t));
    auto output_matrix = output_matrix_t->matrix<float>();

    // Iterates through tensor slices and copies over values from the old tensor
    // to the output matrix.
    int64_t row_index = min_old_row;
    int64_t rows_copied = 0;
    Tensor loaded_tensor_t;
    for (const TensorSlice& tensor_slice : tensor_slices) {
      LOG(INFO) << "Loading slice " << tensor_slice.DebugString();
      TensorShape slice_shape;
      OP_REQUIRES_OK(context,
                     tensor_slice.SliceTensorShape(tensor_shape, &slice_shape));
      // Potentially re-allocates the tensor buffer since the last slice may
      // have fewer rows than the other slices.
      if (loaded_tensor_t.shape() != slice_shape) {
        loaded_tensor_t = Tensor(DT_FLOAT, slice_shape);
      }
      OP_REQUIRES_OK(context, reader.LookupSlice(old_tensor_name, tensor_slice,
                                                 &loaded_tensor_t));

      // Iterates through the old loaded tensor slice row-by-row.
      for (int row = 0; row < loaded_tensor_t.dim_size(0); ++row, ++row_index) {
        if (row_index % 500000 == min_old_row) {
          LOG(INFO) << "Processing old row " << row_index;
        }

        // If the old row ID is not found in old_row_to_new_row_map, continue
        // to the next row; otherwise, copy it to the output matrix.
        const int64_t* new_row_ptr =
            gtl::FindOrNull(old_row_to_new_row_map, row_index);
        if (new_row_ptr == nullptr) {
          continue;
        }
        ++rows_copied;
        const int64_t new_row = *new_row_ptr;

        // Copies over the row element-by-element, in case remapping is needed
        // along the column axis.
        const auto& loaded_tensor = loaded_tensor_t.matrix<float>();
        for (int old_col = 0; old_col < loaded_tensor_t.dim_size(1);
             ++old_col) {
          int64_t new_col = old_col;
          if (remap_cols) {
            const int64_t* new_col_ptr =
                gtl::FindOrNull(old_col_to_new_col_map, old_col);
            if (new_col_ptr == nullptr) {
              // Column remapping is specified, but this column is not found in
              // old_col_to_new_col_map, so we leave it uninitialized, to be
              // filled in with initializing_values later.
              continue;
            }
            new_col = *new_col_ptr;
          }

          OP_REQUIRES(context,
                      new_row < num_rows_ && new_col < num_cols_ &&
                          new_row >= 0 && new_col >= 0,
                      errors::Internal(strings::StrCat(
                          "new_row=", new_row, " and new_col=", new_col,
                          " should have been less than num_rows_=", num_rows_,
                          " and num_cols_=", num_cols_,
                          " and non-negative. This should never have happened "
                          "if the code were correct. Please file a bug.")));
          output_matrix(new_row, new_col) = loaded_tensor(row, old_col);
        }
      }
    }
    LOG(INFO) << "Copied " << rows_copied << " rows from old matrix (with "
              << tensor_shape.dim_size(0) << " rows) to new matrix (with "
              << num_rows_ << " rows).";

    // At this point, there are potentially whole rows/columns uninitialized
    // (corresponding to the indices where row_id_present/col_id_present are
    // false). We fill this in cell-by-cell using row_id_present and
    // col_id_present while dequeuing from the initializing_values vector.
    const Tensor* initializing_values_t;
    OP_REQUIRES_OK(
        context, context->input("initializing_values", &initializing_values_t));
    const auto initializing_values = initializing_values_t->flat<float>();
    int64_t initializing_values_index = 0;
    for (int i = 0; i < num_rows_; ++i) {
      for (int j = 0; j < num_cols_; ++j) {
        if (row_id_present[i] && col_id_present[j]) continue;
        OP_REQUIRES(
            context, initializing_values_index < initializing_values.size(),
            errors::InvalidArgument(
                "initializing_values contained ", initializing_values.size(),
                " elements, but more missing values remain."));
        output_matrix(i, j) = initializing_values(initializing_values_index);
        ++initializing_values_index;
      }
    }

    // Checks that we used all the given initializing values.
    OP_REQUIRES(
        context, initializing_values_index == initializing_values.size(),
        errors::InvalidArgument(
            "initializing_values contained ", initializing_values.size(),
            " elements, but only ", initializing_values_index,
            " elements were used to fill in missing values."));
  }

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.LoadAndRemapMatrix does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. The code assumes `initializing_values` is a vector but there is no validation for this before accessing its value. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.


Prevent heap OOB in sparse reduction ops.

PiperOrigin-RevId: 387934524
Change-Id: I894aa30f1e454f09b471d565b4a325da49322c1a

  void Compute(OpKernelContext *ctx) override {
    const Tensor *indices_t, *values_t, *shape_t, *reduction_axes_t;
    OP_REQUIRES_OK(ctx, ctx->input("input_indices", &indices_t));
    OP_REQUIRES_OK(ctx, ctx->input("input_values", &values_t));
    OP_REQUIRES_OK(ctx, ctx->input("input_shape", &shape_t));
    OP_REQUIRES_OK(ctx, ctx->input("reduction_axes", &reduction_axes_t));

    OP_REQUIRES_OK(ctx, ValidateInputs(shape_t, reduction_axes_t));

    // TODO(zongheng): we will call Reorder() below, which will modify
    // in-place the underlying indices and values buffers.  To avoid
    // surprises of this kernel being stateful, we work around the above by
    // making deep copies here.  Remove this if/when we change Reorder()'s
    // semantics.
    const auto shape_vec = shape_t->vec<int64>();
    SparseTensor sp;
    OP_REQUIRES_OK(ctx, SparseTensor::Create(
        tensor::DeepCopy(*indices_t), tensor::DeepCopy(*values_t),
                    TensorShape(shape_vec), &sp));
    ReduceDetails reduction = SparseTensorReduceHelper(
        sp, reduction_axes_t->flat<int32>(), keep_dims_);

    Tensor *out_values;
    OP_REQUIRES_OK(
        ctx, ctx->allocate_output(0, reduction.reduced_shape, &out_values));
    auto out_flat = out_values->flat<T>();
    out_flat.setZero();

    Tensor tmp_reduced_val;
    OP_REQUIRES_OK(ctx, ctx->allocate_temp(DataTypeToEnum<T>::value,
                                           TensorShape({}), &tmp_reduced_val));
    auto reduced_val = tmp_reduced_val.scalar<T>();

    // Compute strides, and use it to convert coords to flat index.  The
    // coordinates returned by .group() have the same ndims as group_by_dims.
    gtl::InlinedVector<int64, 8> output_strides(reduction.group_by_dims.size());
    if (!output_strides.empty()) {  // Do this iff we don't reduce all.
      output_strides.back() = 1;
      for (int d = output_strides.size() - 2; d >= 0; --d) {
        output_strides[d] =
            output_strides[d + 1] * shape_vec(reduction.group_by_dims[d + 1]);
      }
    }

    auto CoordinatesToFlatIndex = [](ArraySlice<int64> coords,
                                     ArraySlice<int64> strides) -> int64 {
      if (strides.empty()) {  // Reduce all.
        return 0;
      }
      CHECK_EQ(coords.size(), strides.size());
      int64_t idx = 0;
      for (int i = 0; i < coords.size(); ++i) {
        idx += coords[i] * strides[i];
      }
      return idx;
    };

    // Each group maps one-on-one onto a value in the reduced tensor.
    // g.group() provides the coordinates of a particular reduced value.
    sp.Reorder<T>(reduction.reorder_dims);
    for (const auto &g : sp.group(reduction.group_by_dims)) {
      Op::template Run<T>(ctx, reduced_val, g.template values<T>());
      const int64_t idx = CoordinatesToFlatIndex(g.group(), output_strides);
      out_flat(idx) = reduced_val();
      VLOG(2) << "coords: " << absl::StrJoin(g.group(), ",")
              << "; idx: " << idx << "; group " << Op::Name() << ": "
              << reduced_val();
    }
  }

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of sparse reduction operations in TensorFlow can trigger accesses outside of bounds of heap allocated data. The [implementation](https://github.com/tensorflow/tensorflow/blob/a1bc56203f21a5a4995311825ffaba7a670d7747/tensorflow/core/kernels/sparse_reduce_op.cc#L217-L228) fails to validate that each reduction group does not overflow and that each corresponding index does not point to outside the bounds of the input tensor. We have patched the issue in GitHub commit 87158f43f05f2720a374f3e6d22a7aaa3a33f750. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.