Moderate severityNVD Advisory· Published Sep 25, 2020· Updated Aug 4, 2024

Denial of service in tensorflow-lite

CVE-2020-15213

Description

In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom Verifier to limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, if the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.

Affected packages

Versions sourced from the GitHub Security Advisory.

Package	Affected versions	Patched versions
tensorflowPyPI	>= 2.2.0, < 2.2.1	2.2.1
tensorflowPyPI	>= 2.3.0, < 2.3.1	2.3.1
tensorflow-cpuPyPI	>= 2.2.0, < 2.2.1	2.2.1
tensorflow-cpuPyPI	>= 2.3.0, < 2.3.1	2.3.1
tensorflow-gpuPyPI	>= 2.2.0, < 2.2.1	2.2.1
tensorflow-gpuPyPI	>= 2.3.0, < 2.3.1	2.3.1

Affected products

Tensorflow/Tensorflowv5
Range: = 2.2.0

Patches

a4030d8ba369

[tflite] Validate segment ids for segment_sum.

https://github.com/tensorflow/tensorflowMihai MaruseacSep 18, 2020via ghsa

commit

2 files changed · +48 −3

tensorflow/lite/kernels/segment_sum.cc+16 −3 modified

@@ -32,11 +32,24 @@ TfLiteStatus ResizeOutputTensor(TfLiteContext* context,
                                 const TfLiteTensor* data,
                                 const TfLiteTensor* segment_ids,
                                 TfLiteTensor* output) {
-  int max_index = -1;
+  // Segment ids should be of same cardinality as first input dimension and they
+  // should be increasing by at most 1, from 0 (e.g., [0, 0, 1, 2, 3] is valid)
   const int segment_id_size = segment_ids->dims->data[0];
-  if (segment_id_size > 0) {
-    max_index = segment_ids->data.i32[segment_id_size - 1];
+  TF_LITE_ENSURE_EQ(context, segment_id_size, data->dims->data[0]);
+  int previous_segment_id = -1;
+  for (int i = 0; i < segment_id_size; i++) {
+    const int current_segment_id = GetTensorData<int32_t>(segment_ids)[i];
+    if (i == 0) {
+      TF_LITE_ENSURE_EQ(context, current_segment_id, 0);
+    } else {
+      int delta = current_segment_id - previous_segment_id;
+      TF_LITE_ENSURE(context, delta == 0 || delta == 1);
+    }
+    previous_segment_id = current_segment_id;
   }
+
+  const int max_index = previous_segment_id;
+
   const int data_rank = NumDimensions(data);
   TfLiteIntArray* output_shape = TfLiteIntArrayCreate(NumDimensions(data));
   output_shape->data[0] = max_index + 1;

tensorflow/lite/kernels/segment_sum_test.cc+32 −0 modified

@@ -108,5 +108,37 @@ TEST(SegmentSumOpModelTest, Float32Test_ThreeDimensions) {
   EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({2, 2, 1}));
 }
 
+TEST(SegmentSumOpModelTest, TestFailIfSegmentsAreNotSorted) {
+  SegmentSumOpModel<int32_t> model({TensorType_INT32, {3, 2}},
+                                   {TensorType_INT32, {3}});
+  model.PopulateTensor<int32_t>(model.data(), {1, 2, 3, 4, 5, 6});
+  model.PopulateTensor<int32_t>(model.segment_ids(), {0, 3, 1});
+  ASSERT_EQ(model.InvokeUnchecked(), kTfLiteError);
+}
+
+TEST(SegmentSumOpModelTest, TestFailIfSegmentsAreNotConsecutive) {
+  SegmentSumOpModel<int32_t> model({TensorType_INT32, {3, 2}},
+                                   {TensorType_INT32, {3}});
+  model.PopulateTensor<int32_t>(model.data(), {1, 2, 3, 4, 5, 6});
+  model.PopulateTensor<int32_t>(model.segment_ids(), {0, 3, 5});
+  ASSERT_EQ(model.InvokeUnchecked(), kTfLiteError);
+}
+
+TEST(SegmentSumOpModelTest, TestFailIfSegmentsAreNegative) {
+  SegmentSumOpModel<int32_t> model({TensorType_INT32, {3, 2}},
+                                   {TensorType_INT32, {3}});
+  model.PopulateTensor<int32_t>(model.data(), {1, 2, 3, 4, 5, 6});
+  model.PopulateTensor<int32_t>(model.segment_ids(), {-1, 0, 1});
+  ASSERT_EQ(model.InvokeUnchecked(), kTfLiteError);
+}
+
+TEST(SegmentSumOpModelTest, TestFailIfSegmentsAreNotTheRightCardinality) {
+  SegmentSumOpModel<int32_t> model({TensorType_INT32, {3, 2}},
+                                   {TensorType_INT32, {2}});
+  model.PopulateTensor<int32_t>(model.data(), {1, 2, 3, 4, 5, 6});
+  model.PopulateTensor<int32_t>(model.segment_ids(), {0, 1});
+  ASSERT_EQ(model.InvokeUnchecked(), kTfLiteError);
+}
+
 }  // namespace
 }  // namespace tflite

204945b19e44

[tflite] Validate segment ids for segment_sum.

https://github.com/tensorflow/tensorflowMihai MaruseacSep 18, 2020via ghsa

commit

2 files changed · +48 −3

tensorflow/lite/kernels/segment_sum.cc+16 −3 modified

@@ -34,11 +34,24 @@ TfLiteStatus ResizeOutputTensor(TfLiteContext* context,
                                 const TfLiteTensor* data,
                                 const TfLiteTensor* segment_ids,
                                 TfLiteTensor* output) {
-  int max_index = -1;
+  // Segment ids should be of same cardinality as first input dimension and they
+  // should be increasing by at most 1, from 0 (e.g., [0, 0, 1, 2, 3] is valid)
   const int segment_id_size = segment_ids->dims->data[0];
-  if (segment_id_size > 0) {
-    max_index = segment_ids->data.i32[segment_id_size - 1];
+  TF_LITE_ENSURE_EQ(context, segment_id_size, data->dims->data[0]);
+  int previous_segment_id = -1;
+  for (int i = 0; i < segment_id_size; i++) {
+    const int current_segment_id = GetTensorData<int32_t>(segment_ids)[i];
+    if (i == 0) {
+      TF_LITE_ENSURE_EQ(context, current_segment_id, 0);
+    } else {
+      int delta = current_segment_id - previous_segment_id;
+      TF_LITE_ENSURE(context, delta == 0 || delta == 1);
+    }
+    previous_segment_id = current_segment_id;
   }
+
+  const int max_index = previous_segment_id;
+
   const int data_rank = NumDimensions(data);
   TfLiteIntArray* output_shape = TfLiteIntArrayCreate(NumDimensions(data));
   output_shape->data[0] = max_index + 1;

tensorflow/lite/kernels/segment_sum_test.cc+32 −0 modified

@@ -110,5 +110,37 @@ TEST(SegmentSumOpModelTest, Float32Test_ThreeDimensions) {
   EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({2, 2, 1}));
 }
 
+TEST(SegmentSumOpModelTest, TestFailIfSegmentsAreNotSorted) {
+  SegmentSumOpModel<int32_t> model({TensorType_INT32, {3, 2}},
+                                   {TensorType_INT32, {3}});
+  model.PopulateTensor<int32_t>(model.data(), {1, 2, 3, 4, 5, 6});
+  model.PopulateTensor<int32_t>(model.segment_ids(), {0, 3, 1});
+  ASSERT_EQ(model.InvokeUnchecked(), kTfLiteError);
+}
+
+TEST(SegmentSumOpModelTest, TestFailIfSegmentsAreNotConsecutive) {
+  SegmentSumOpModel<int32_t> model({TensorType_INT32, {3, 2}},
+                                   {TensorType_INT32, {3}});
+  model.PopulateTensor<int32_t>(model.data(), {1, 2, 3, 4, 5, 6});
+  model.PopulateTensor<int32_t>(model.segment_ids(), {0, 3, 5});
+  ASSERT_EQ(model.InvokeUnchecked(), kTfLiteError);
+}
+
+TEST(SegmentSumOpModelTest, TestFailIfSegmentsAreNegative) {
+  SegmentSumOpModel<int32_t> model({TensorType_INT32, {3, 2}},
+                                   {TensorType_INT32, {3}});
+  model.PopulateTensor<int32_t>(model.data(), {1, 2, 3, 4, 5, 6});
+  model.PopulateTensor<int32_t>(model.segment_ids(), {-1, 0, 1});
+  ASSERT_EQ(model.InvokeUnchecked(), kTfLiteError);
+}
+
+TEST(SegmentSumOpModelTest, TestFailIfSegmentsAreNotTheRightCardinality) {
+  SegmentSumOpModel<int32_t> model({TensorType_INT32, {3, 2}},
+                                   {TensorType_INT32, {2}});
+  model.PopulateTensor<int32_t>(model.data(), {1, 2, 3, 4, 5, 6});
+  model.PopulateTensor<int32_t>(model.segment_ids(), {0, 1});
+  ASSERT_EQ(model.InvokeUnchecked(), kTfLiteError);
+}
+
 }  // namespace
 }  // namespace tflite

00c7ed7ce81c

[tflite] Validate segment ids for segment_sum.

https://github.com/tensorflow/tensorflowMihai MaruseacSep 18, 2020via ghsa

commit

2 files changed · +48 −3

tensorflow/lite/kernels/segment_sum.cc+16 −3 modified

@@ -34,11 +34,24 @@ TfLiteStatus ResizeOutputTensor(TfLiteContext* context,
                                 const TfLiteTensor* data,
                                 const TfLiteTensor* segment_ids,
                                 TfLiteTensor* output) {
-  int max_index = -1;
+  // Segment ids should be of same cardinality as first input dimension and they
+  // should be increasing by at most 1, from 0 (e.g., [0, 0, 1, 2, 3] is valid)
   const int segment_id_size = segment_ids->dims->data[0];
-  if (segment_id_size > 0) {
-    max_index = segment_ids->data.i32[segment_id_size - 1];
+  TF_LITE_ENSURE_EQ(context, segment_id_size, data->dims->data[0]);
+  int previous_segment_id = -1;
+  for (int i = 0; i < segment_id_size; i++) {
+    const int current_segment_id = GetTensorData<int32_t>(segment_ids)[i];
+    if (i == 0) {
+      TF_LITE_ENSURE_EQ(context, current_segment_id, 0);
+    } else {
+      int delta = current_segment_id - previous_segment_id;
+      TF_LITE_ENSURE(context, delta == 0 || delta == 1);
+    }
+    previous_segment_id = current_segment_id;
   }
+
+  const int max_index = previous_segment_id;
+
   const int data_rank = NumDimensions(data);
   TfLiteIntArray* output_shape = TfLiteIntArrayCreate(NumDimensions(data));
   output_shape->data[0] = max_index + 1;

tensorflow/lite/kernels/segment_sum_test.cc+32 −0 modified

@@ -110,5 +110,37 @@ TEST(SegmentSumOpModelTest, Float32Test_ThreeDimensions) {
   EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({2, 2, 1}));
 }
 
+TEST(SegmentSumOpModelTest, TestFailIfSegmentsAreNotSorted) {
+  SegmentSumOpModel<int32_t> model({TensorType_INT32, {3, 2}},
+                                   {TensorType_INT32, {3}});
+  model.PopulateTensor<int32_t>(model.data(), {1, 2, 3, 4, 5, 6});
+  model.PopulateTensor<int32_t>(model.segment_ids(), {0, 3, 1});
+  ASSERT_EQ(model.InvokeUnchecked(), kTfLiteError);
+}
+
+TEST(SegmentSumOpModelTest, TestFailIfSegmentsAreNotConsecutive) {
+  SegmentSumOpModel<int32_t> model({TensorType_INT32, {3, 2}},
+                                   {TensorType_INT32, {3}});
+  model.PopulateTensor<int32_t>(model.data(), {1, 2, 3, 4, 5, 6});
+  model.PopulateTensor<int32_t>(model.segment_ids(), {0, 3, 5});
+  ASSERT_EQ(model.InvokeUnchecked(), kTfLiteError);
+}
+
+TEST(SegmentSumOpModelTest, TestFailIfSegmentsAreNegative) {
+  SegmentSumOpModel<int32_t> model({TensorType_INT32, {3, 2}},
+                                   {TensorType_INT32, {3}});
+  model.PopulateTensor<int32_t>(model.data(), {1, 2, 3, 4, 5, 6});
+  model.PopulateTensor<int32_t>(model.segment_ids(), {-1, 0, 1});
+  ASSERT_EQ(model.InvokeUnchecked(), kTfLiteError);
+}
+
+TEST(SegmentSumOpModelTest, TestFailIfSegmentsAreNotTheRightCardinality) {
+  SegmentSumOpModel<int32_t> model({TensorType_INT32, {3, 2}},
+                                   {TensorType_INT32, {2}});
+  model.PopulateTensor<int32_t>(model.data(), {1, 2, 3, 4, 5, 6});
+  model.PopulateTensor<int32_t>(model.segment_ids(), {0, 1});
+  ASSERT_EQ(model.InvokeUnchecked(), kTfLiteError);
+}
+
 }  // namespace
 }  // namespace tflite

Vulnerability mechanics

Generated by null/stub on May 9, 2026. Inputs: CWE entries + fix-commit diffs from this CVE's patches. Citations validated against bundle.

References

github.com/advisories/GHSA-hjmq-236j-8m87ghsaADVISORY
nvd.nist.gov/vuln/detail/CVE-2020-15213ghsaADVISORY
github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2020-293.yamlghsaWEB
github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2020-328.yamlghsaWEB
github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2020-136.yamlghsaWEB
github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/segment_sum.ccghsaWEB
github.com/tensorflow/tensorflow/commit/00c7ed7ce81c2126ebc17dfe7073b5c0efd5ec0aghsaWEB
github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178aghsax_refsource_MISCWEB
github.com/tensorflow/tensorflow/commit/a4030d8ba3692c438997c27be2dd95f3d5f54827ghsaWEB
github.com/tensorflow/tensorflow/releases/tag/v2.3.1ghsax_refsource_MISCWEB
github.com/tensorflow/tensorflow/security/advisories/GHSA-hjmq-236j-8m87ghsax_refsource_CONFIRMWEB

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cvss	0.260
epss	0.000
exploit	0.000
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patch	-0.070
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