db-d2/primevul-codebert-embeddings huggingface dataset:
Commit message: Fix segmentation fault in tf.image.crop_and_resize when boxes is inf or nan

This fix tries to address the issue raised in 42129 where segmentation fault
happened in tf.image.crop_and_resize when boxes is inf or nan.

This fix adds the check to make sure boxes is not inf or nan (isfinite)

This fix fixes 42129.

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>
CVE: CVE-2020-15266
CWE: CWE-787
Vulnerability description: In Tensorflow before version 2.4.0, when the `boxes` argument of `tf.image.crop_and_resize` has a very large value, the CPU kernel implementation receives it as a C++ `nan` floating point value. Attempting to operate on this is undefined behavior which later produces a segmentation fault. The issue is patched in eccb7ec454e6617738554a255d77f08e60ee0808 and TensorFlow 2.4.0 will be released containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.

Project: tensorflow
File: crop_and_resize_op.cc

static inline Status ParseAndCheckBoxSizes(const Tensor& boxes,
                                           const Tensor& box_index,
                                           int* num_boxes) {
  if (boxes.NumElements() == 0 && box_index.NumElements() == 0) {
    *num_boxes = 0;
    return Status::OK();
  }
  // The shape of 'boxes' is [num_boxes, 4].
  if (boxes.dims() != 2) {
    return errors::InvalidArgument("boxes must be 2-D",
                                   boxes.shape().DebugString());
  }
  *num_boxes = boxes.dim_size(0);
  if (boxes.dim_size(1) != 4) {
    return errors::InvalidArgument("boxes must have 4 columns");
  }
  // The shape of 'box_index' is [num_boxes].
  if (box_index.dims() != 1) {
    return errors::InvalidArgument("box_index must be 1-D",
                                   box_index.shape().DebugString());
  }
  if (box_index.dim_size(0) != *num_boxes) {
    return errors::InvalidArgument("box_index has incompatible shape");
  }
  return Status::OK();
}
Commit message: WindowsServiceControl: quote service binary path

Fix unquoted service path vulnerability.

Closes #657.
CVE: CVE-2020-15261
CWE: CWE-295
Vulnerability description: On Windows the Veyon Service before version 4.4.2 contains an unquoted service path vulnerability, allowing locally authenticated users with administrative privileges to run malicious executables with LocalSystem privileges. Since Veyon users (both students and teachers) usually don't have administrative privileges, this vulnerability is only dangerous in anyway unsafe setups. The problem has been fixed in version 4.4.2. As a workaround, the exploitation of the vulnerability can be prevented by revoking administrative privileges from all potentially untrustworthy users.

Project: veyon
File: WindowsServiceControl.cpp

bool WindowsServiceControl::install( const QString& filePath, const QString& displayName  )
{
	m_serviceHandle = CreateService(
				m_serviceManager,		// SCManager database
				WindowsCoreFunctions::toConstWCharArray( m_name ),	// name of service
				WindowsCoreFunctions::toConstWCharArray( displayName ),// name to display
				SERVICE_ALL_ACCESS,	// desired access
				SERVICE_WIN32_OWN_PROCESS,
				// service type
				SERVICE_AUTO_START,	// start type
				SERVICE_ERROR_NORMAL,	// error control type
				WindowsCoreFunctions::toConstWCharArray( filePath ),		// service's binary
				nullptr,			// no load ordering group
				nullptr,			// no tag identifier
				L"Tcpip\0RpcSs\0\0",		// dependencies
				nullptr,			// LocalSystem account
				nullptr );			// no password

	if( m_serviceHandle == nullptr )
	{
		const auto error = GetLastError();
		if( error == ERROR_SERVICE_EXISTS )
		{
			vCritical() << qUtf8Printable( tr( "The service \"%1\" is already installed." ).arg( m_name ) );
		}
		else
		{
			vCritical() << qUtf8Printable( tr( "The service \"%1\" could not be installed." ).arg( m_name ) );
		}

		return false;
	}

	SC_ACTION serviceActions;
	serviceActions.Delay = 10000;
	serviceActions.Type = SC_ACTION_RESTART;

	SERVICE_FAILURE_ACTIONS serviceFailureActions;
	serviceFailureActions.dwResetPeriod = 0;
	serviceFailureActions.lpRebootMsg = nullptr;
	serviceFailureActions.lpCommand = nullptr;
	serviceFailureActions.lpsaActions = &serviceActions;
	serviceFailureActions.cActions = 1;
	ChangeServiceConfig2( m_serviceHandle, SERVICE_CONFIG_FAILURE_ACTIONS, &serviceFailureActions );

	// Everything went fine
	vInfo() << qUtf8Printable( tr( "The service \"%1\" has been installed successfully." ).arg( m_name ) );

	return true;
}
Commit message: actually return empty stringand not 'em,pty string' as expected
CVE: CVE-2021-27815
CWE: CWE-476
Vulnerability description: NULL Pointer Deference in the exif command line tool, when printing out XML formatted EXIF data, in exif v0.6.22 and earlier allows attackers to cause a Denial of Service (DoS) by uploading a malicious JPEG file, causing the application to crash.

Project: exif
File: actions.c

escape_xml(const char *text)
{
	static char *escaped;
	static size_t escaped_size;
	char *out;
	size_t len;

	if (!strlen(text)) return "empty string";

	for (out=escaped, len=0; *text; ++len, ++out, ++text) {
		/* Make sure there's plenty of room for a quoted character */
		if ((len + 8) > escaped_size) {
			char *bigger_escaped;
			escaped_size += 128;
			bigger_escaped = realloc(escaped, escaped_size);
			if (!bigger_escaped) {
				free(escaped);	/* avoid leaking memory */
				escaped = NULL;
				escaped_size = 0;
				/* Error string is cleverly chosen to fail XML validation */
				return ">>> out of memory <<<";
			}
			out = bigger_escaped + len;
			escaped = bigger_escaped;
		}
		switch (*text) {
			case '&':
				strcpy(out, "&amp;");
				len += strlen(out) - 1;
				out = escaped + len;
				break;
			case '<':
				strcpy(out, "&lt;");
				len += strlen(out) - 1;
				out = escaped + len;
				break;
			case '>':
				strcpy(out, "&gt;");
				len += strlen(out) - 1;
				out = escaped + len;
				break;
			default:
				*out = *text;
				break;
		}
	}
	*out = '\x0';  /* NUL terminate the string */
	return escaped;
}
Commit message: Validate min and max arguments to `QuantizedResizeBilinear`.

PiperOrigin-RevId: 369765091
Change-Id: I33be8b78273ab7d08b97541692fe05cb7f94963a
CVE: CVE-2021-29537
CWE: CWE-787
Vulnerability description: TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `QuantizedResizeBilinear` by passing in invalid thresholds for the quantization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/50711818d2e61ccce012591eeb4fdf93a8496726/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L705-L706) assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly. 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.

Project: tensorflow
File: quantized_resize_bilinear_op.cc

  void Compute(OpKernelContext* context) override {
    const float in_min = context->input(2).flat<float>()(0);
    const float in_max = context->input(3).flat<float>()(0);

    ImageResizerState st(align_corners_, false);
    st.ValidateAndCreateOutput(context);

    if (!context->status().ok()) return;

    // Return if the output is empty.
    if (st.output->NumElements() == 0) return;

    typename TTypes<T, 4>::ConstTensor image_data(
        context->input(0).tensor<T, 4>());
    typename TTypes<T, 4>::Tensor output_data(st.output->tensor<T, 4>());

    ResizeBilinear<T>(image_data, st.height_scale, st.width_scale, in_min,
                      in_max, half_pixel_centers_, &output_data);
    Tensor* out_min = nullptr;
    OP_REQUIRES_OK(context, context->allocate_output(1, {}, &out_min));
    out_min->flat<float>()(0) = in_min;

    Tensor* out_max = nullptr;
    OP_REQUIRES_OK(context, context->allocate_output(2, {}, &out_max));
    out_max->flat<float>()(0) = in_max;
  }
Commit message: Fix heap buffer overflow in tf.raw_ops.UnicodeEncode.

PiperOrigin-RevId: 371717714
Change-Id: If33443b28f158e58078f1268f6b92f2728d219e0
CVE: CVE-2021-29559
CWE: CWE-787
Vulnerability description: TensorFlow is an end-to-end open source platform for machine learning. An attacker can access data outside of bounds of heap allocated array in `tf.raw_ops.UnicodeEncode`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/472c1f12ad9063405737679d4f6bd43094e1d36d/tensorflow/core/kernels/unicode_ops.cc) assumes that the `input_value`/`input_splits` pair specify a valid sparse tensor. 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.

Project: tensorflow
File: unicode_ops.cc

  void Compute(OpKernelContext* context) override {
    // Get inputs
    const Tensor& input_tensor = context->input(0);
    const auto input_tensor_flat = input_tensor.flat<int32>();
    const Tensor& input_splits = context->input(1);
    const auto input_splits_flat = input_splits.flat<SPLITS_TYPE>();

    // Since we limit to a 2-D input (flat_values of rank 1 and a single splits
    // tensor), our output dimension will be 1 with it's size equal to the
    // number of splits (outer dimension or ragged tensor).
    TensorShape output_shape({input_splits.dim_size(0) - 1});
    Tensor* output_tensor;
    OP_REQUIRES_OK(context, context->allocate_output("output", output_shape,
                                                     &output_tensor));
    auto output_tensor_flat = output_tensor->flat<tstring>();

    // Use a single index over the flattened input values tensor.
    int idx = 0;
    // Loop through our split dimension to create a new string at each split.
    for (int i = 1; i < input_splits_flat.size(); ++i) {
      icu::UnicodeString unicode_string;
      icu::UnicodeStringAppendable appendable_unicode_string(unicode_string);
      for (; idx < input_splits_flat(i); ++idx) {
        int32 code_point = input_tensor_flat(idx);
        // Check for invalid code point
        if (!U_IS_UNICODE_CHAR(code_point)) {
          if (error_options_.error_on_malformatting) {
            context->CtxFailure(errors::InvalidArgument(
                "Code point is out of range for Unicode, or a noncharacter."));
            return;
          } else if (!error_options_.elide_replacement) {
            code_point = error_options_.subst;
          }
        }
        appendable_unicode_string.appendCodePoint(code_point);
      }
      // Encode our string and save in the output.
      tstring result;
      Encode(encoding_, unicode_string, &result);
      output_tensor_flat(i - 1) = std::move(result);
    }
  }
Commit message: Fix heap buffer overflow

PiperOrigin-RevId: 372132844
Change-Id: Idef9895efaf145f2b1c23d31983601ec980cd5e4
CVE: CVE-2021-29570
CWE: CWE-125
Vulnerability description: TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation(https://github.com/tensorflow/tensorflow/blob/ef0c008ee84bad91ec6725ddc42091e19a30cf0e/tensorflow/core/kernels/maxpooling_op.cc#L1016-L1017) uses the same value to index in two different arrays but there is no guarantee that the sizes are identical. 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.

Project: tensorflow
File: maxpooling_op.cc

  static void launch(OpKernelContext* context, const PoolParameters& params,
                     const Tensor& grad_in, const Tensor& argmax,
                     Tensor* grad_out, const bool include_batch_in_index) {
    const DeviceBase::CpuWorkerThreads& worker_threads =
        *(context->device()->tensorflow_cpu_worker_threads());

    auto shard = [&grad_in, &argmax, &grad_out, include_batch_in_index](
                     int64 start, int64 limit) {
      const int64 batch_size =
          GetTensorDim(grad_out->shape(), FORMAT_NHWC, 'N');
      const int64 output_size_per_batch = grad_out->NumElements() / batch_size;
      const int64 input_size_per_batch = grad_in.NumElements() / batch_size;

      {
        auto grad_out_flat = grad_out->flat<T>();
        auto argmax_flat = argmax.flat<int64>();
        auto grad_in_flat = grad_in.flat<T>();

        const int64 output_start = start * output_size_per_batch;
        const int64 output_end = limit * output_size_per_batch;
        EigenMatrixMap inputShard(grad_out_flat.data() + output_start, 1,
                                  output_end - output_start);
        inputShard.setConstant(T(0));

        const int input_start = start * input_size_per_batch;
        const int input_end = limit * input_size_per_batch;
        for (int64 index = input_start; index < input_end; index++) {
          int64 grad_out_index = argmax_flat(index);
          if (!include_batch_in_index) {
            const int64 cur_batch = index / input_size_per_batch;
            grad_out_index += cur_batch * output_size_per_batch;
          }
          CHECK(grad_out_index >= output_start && grad_out_index < output_end)
              << "Invalid output gradient index: " << grad_out_index << ", "
              << output_start << ", " << output_end;
          grad_out_flat(grad_out_index) += grad_in_flat(index);
        }
      }
    };

    const int64 batch_size = GetTensorDim(grad_out->shape(), FORMAT_NHWC, 'N');
    const int64 shard_cost = grad_out->NumElements() / batch_size;
    Shard(worker_threads.num_threads, worker_threads.workers, batch_size,
          shard_cost, shard);
  }
Commit message: Ensure non-empty compressed input in tf.raw_ops.UncompressElement

PiperOrigin-RevId: 383955815
Change-Id: I072a84fd02738dd2f51b3f42836ed80067dba4a8
CVE: CVE-2021-37649
CWE: CWE-476
Vulnerability description: TensorFlow is an end-to-end open source platform for machine learning. The code for `tf.raw_ops.UncompressElement` can be made to trigger a null pointer dereference. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/data/experimental/compression_ops.cc#L50-L53) obtains a pointer to a `CompressedElement` from a `Variant` tensor and then proceeds to dereference it for decompressing. There is no check that the `Variant` tensor contained a `CompressedElement`, so the pointer is actually `nullptr`. We have patched the issue in GitHub commit 7bdf50bb4f5c54a4997c379092888546c97c3ebd. 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.

Project: tensorflow

void UncompressElementOp::Compute(OpKernelContext* ctx) {
  Tensor tensor = ctx->input(0);
  const Variant& variant = tensor.scalar<Variant>()();
  const CompressedElement* compressed = variant.get<CompressedElement>();

  std::vector<Tensor> components;
  OP_REQUIRES_OK(ctx, UncompressElement(*compressed, &components));
  OP_REQUIRES(ctx, components.size() == output_types_.size(),
              errors::FailedPrecondition("Expected ", output_types_.size(),
                                         " outputs from uncompress, but got ",
                                         components.size()));
  for (int i = 0; i < components.size(); ++i) {
    OP_REQUIRES(
        ctx, components[i].dtype() == output_types_[i],
        errors::FailedPrecondition("Expected a tensor of type ",
                                   DataTypeString(output_types_[i]),
                                   " but got a tensor of type ",
                                   DataTypeString(components[i].dtype())));
    ctx->set_output(i, components[i]);
  }
}
Commit message: Verify UntrustedCall output is outside enclave

PiperOrigin-RevId: 333781703
Change-Id: I9df55c04dc8b04f4bf0bda8e68cc32bca81b933a
CVE: CVE-2020-8936
CWE: CWE-125
Vulnerability description: An arbitrary memory overwrite vulnerability in Asylo versions up to 0.6.0 allows an attacker to make a host call to UntrustedCall. UntrustedCall failed to validate the buffer range within sgx_params and allowed the host to return a pointer that was an address within the enclave memory. This allowed an attacker to read memory values from within the enclave.

Project: asylo
File: trusted_sgx.cc

PrimitiveStatus TrustedPrimitives::UntrustedCall(uint64_t untrusted_selector,
                                                 MessageWriter *input,
                                                 MessageReader *output) {
  int ret;

  UntrustedCacheMalloc *untrusted_cache = UntrustedCacheMalloc::Instance();

  SgxParams *const sgx_params =
      reinterpret_cast<SgxParams *>(untrusted_cache->Malloc(sizeof(SgxParams)));
  Cleanup clean_up(
      [sgx_params, untrusted_cache] { untrusted_cache->Free(sgx_params); });
  sgx_params->input_size = 0;
  sgx_params->input = nullptr;
  if (input) {
    sgx_params->input_size = input->MessageSize();
    if (sgx_params->input_size > 0) {
      // Allocate and copy data to |input_buffer|.
      sgx_params->input = untrusted_cache->Malloc(sgx_params->input_size);
      input->Serialize(const_cast<void *>(sgx_params->input));
    }
  }
  sgx_params->output_size = 0;
  sgx_params->output = nullptr;
  CHECK_OCALL(
      ocall_dispatch_untrusted_call(&ret, untrusted_selector, sgx_params));
  if (sgx_params->input) {
    untrusted_cache->Free(const_cast<void *>(sgx_params->input));
  }
  if (sgx_params->output) {
    // For the results obtained in |output_buffer|, copy them to |output|
    // before freeing the buffer.
    output->Deserialize(sgx_params->output, sgx_params->output_size);
    TrustedPrimitives::UntrustedLocalFree(sgx_params->output);
  }
  return PrimitiveStatus::OkStatus();
}
Commit message: CVE-2021-43824

jwt_atuhn: fixed the crash when a CONNECT request is sent to JWT filter
configured with regex match.

Signed-off-by: Yan Avlasov <yavlasov@google.com>
CVE: CVE-2021-43824
CWE: CWE-703
Vulnerability description: Envoy is an open source edge and service proxy, designed for cloud-native applications. In affected versions a crafted request crashes Envoy when a CONNECT request is sent to JWT filter configured with regex match. This provides a denial of service attack vector. The only workaround is to not use regex in the JWT filter. Users are advised to upgrade.

Project: envoy
File: matcher.cc

  bool matches(const Http::RequestHeaderMap& headers) const override {
    if (BaseMatcherImpl::matchRoute(headers)) {
      const Http::HeaderString& path = headers.Path()->value();
      const absl::string_view query_string = Http::Utility::findQueryStringStart(path);
      absl::string_view path_view = path.getStringView();
      path_view.remove_suffix(query_string.length());
      if (path_matcher_->match(path_view)) {
        ENVOY_LOG(debug, "Regex requirement '{}' matched.", regex_str_);
        return true;
      }
    }
    return false;
  }
Commit message: Fix buffer overflow in M_LoadDefaults

Too much data will most likely result in a crash or freeze, but you can overwrite the stack which can be used to do an arbitrary code execution. (https://twitter.com/notrevenant/status/1268654123903340544)
CVE: CVE-2020-15007
CWE: CWE-787
Vulnerability description: A buffer overflow in the M_LoadDefaults function in m_misc.c in id Tech 1 (aka Doom engine) allows arbitrary code execution via an unsafe usage of fscanf, because it does not limit the number of characters to be read in a format argument.

Project: doom-vanille
File: m_misc.c

void M_LoadDefaults (void)
{
    int		i;
    int		len;
    FILE*	f;
    char	def[80];
    char	strparm[100];
    char*	newstring;
    int		parm;
    boolean	isstring;
    
    // set everything to base values
    numdefaults = sizeof(defaults)/sizeof(defaults[0]);
    for (i=0 ; i<numdefaults ; i++)
	*defaults[i].location = defaults[i].defaultvalue;
    
    // check for a custom default file
    i = M_CheckParm ("-config");
    if (i && i<myargc-1)
    {
	defaultfile = myargv[i+1];
	printf ("	default file: %s\n",defaultfile);
    }
    else
	defaultfile = basedefault;
    
    // read the file in, overriding any set defaults
    f = fopen (defaultfile, "r");
    if (f)
    {
	while (!feof(f))
	{
	    isstring = false;
	    if (fscanf (f, "%79s %[^\n]\n", def, strparm) == 2)
	    {
		if (strparm[0] == '"')
		{
		    // get a string default
		    isstring = true;
		    len = strlen(strparm);
		    newstring = (char *) malloc(len);
		    strparm[len-1] = 0;
		    strcpy(newstring, strparm+1);
		}
		else if (strparm[0] == '0' && strparm[1] == 'x')
		    sscanf(strparm+2, "%x", &parm);
		else
		    sscanf(strparm, "%i", &parm);
		for (i=0 ; i<numdefaults ; i++)
		    if (!strcmp(def, defaults[i].name))
		    {
			if (!isstring)
			    *defaults[i].location = parm;
			else
			    *defaults[i].location =
				(int) newstring;
			break;
		    }
	    }
	}
		
	fclose (f);
    }

    for (i = 0; i < numdefaults; i++)
    {
        if (defaults[i].scantranslate)
        {
            parm = *defaults[i].location;
            defaults[i].untranslated = parm;
            *defaults[i].location = scantokey[parm];
        }
    }
}
Commit message: Add missing validation to pooling_ops_3d

PiperOrigin-RevId: 372218727
Change-Id: I6b9ed4266aa7286c02f1f230d7bea922c1be547e
CVE: CVE-2021-29574
CWE: CWE-476
Vulnerability description: TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPool3DGradGrad` exhibits undefined behavior by dereferencing null pointers backing attacker-supplied empty tensors. The implementation(https://github.com/tensorflow/tensorflow/blob/72fe792967e7fd25234342068806707bbc116618/tensorflow/core/kernels/pooling_ops_3d.cc#L679-L703) fails to validate that the 3 tensor inputs are not empty. If any of them is empty, then accessing the elements in the tensor results in dereferencing a null pointer. 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.

Project: tensorflow
File: pooling_ops_3d.cc

  void Compute(OpKernelContext* context) override {
    const Tensor& tensor_in = context->input(0);
    const Tensor& tensor_out = context->input(1);
    const Tensor& out_grad_backprop = context->input(2);

    // For maxpooling3d, tensor_in should have 5 dimensions.
    OP_REQUIRES(context, tensor_in.dims() == 5,
                errors::InvalidArgument("tensor_in must be 5-dimensional"));
    OP_REQUIRES(context, tensor_out.dims() == 5,
                errors::InvalidArgument("tensor_out must be 5-dimensional"));
    // For maxpooling3d, out_grad_backprop should have 5 dimensions.
    OP_REQUIRES(
        context, out_grad_backprop.dims() == 5,
        errors::InvalidArgument("out_grad_backprop must be 5-dimensional"));

    Pool3dParameters params{context,  ksize_,       stride_,
                            padding_, data_format_, tensor_in.shape()};

    Tensor* output = nullptr;
    OP_REQUIRES_OK(context, context->forward_input_or_allocate_output(
                                {2}, 0, tensor_out.shape(), &output));

    LaunchMaxPooling3dGradGradOp<Device, T>::launch(
        context, params, tensor_in, tensor_out, out_grad_backprop, output);
  }