CVE-2021-37686

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the strided slice implementation in TFLite has a logic bug which can allow an attacker to trigger an infinite loop. This arises from newly introduced support for ellipsis in axis definition. An attacker can craft a model such that ellipsis_end_idx is smaller than i (e.g., always negative). In this case, the inner loop does not increase i and the continue statement causes execution to skip over the preincrement at the end of the outer loop. We have patched the issue in GitHub commit dfa22b348b70bb89d6d6ec0ff53973bacb4f4695. TensorFlow 2.6.0 is the only affected version.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the strided slice implementation in TFLite has a logic bug which can allow an attacker to trigger an infinite loop. This arises from newly introduced support for ellipsis in axis definition. An attacker can craft a model such that ellipsis_end_idx is smaller than i (e.g., always negative). In this case, the inner loop does not increase i and the continue statement causes execution to skip over the preincrement at the end of the outer loop. We have patched the issue in GitHub commit dfa22b348b70bb89d6d6ec0ff53973bacb4f4695. TensorFlow 2.6.0 is the only affected version.

Impact

The strided slice implementation in TFLite has a logic bug which can allow an attacker to trigger an infinite loop. This arises from newly introduced support for ellipsis in axis definition:

  for (int i = 0; i < effective_dims;) {
    if ((1 << i) & op_context->params->ellipsis_mask) {
      // ...
      int ellipsis_end_idx =
          std::min(i + 1 + num_add_axis + op_context->input_dims - begin_count,
                   effective_dims);
      // ...
      for (; i < ellipsis_end_idx; ++i) {
        // ...
      }
      continue;
    }
    // ...
    ++i;
  }

An attacker can craft a model such that ellipsis_end_idx is smaller than i (e.g., always negative). In this case, the inner loop does not increase i and the continue statement causes execution to skip over the preincrement at the end of the outer loop.

Patches

We have patched the issue in GitHub commit dfa22b348b70bb89d6d6ec0ff53973bacb4f4695.

The fix will be included in TensorFlow 2.6.0. This is the only affected version.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by members of the Aivul Team from Qihoo 360.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the strided slice implementation in TFLite has a logic bug which can allow an attacker to trigger an infinite loop. This arises from newly introduced support for ellipsis in axis definition. An attacker can craft a model such that ellipsis_end_idx is smaller than i (e.g., always negative). In this case, the inner loop does not increase i and the continue statement causes execution to skip over the preincrement at the end of the outer loop. We have patched the issue in GitHub commit dfa22b348b70bb89d6d6ec0ff53973bacb4f4695. TensorFlow 2.6.0 is the only affected version.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the strided slice implementation in TFLite has a logic bug which can allow an attacker to trigger an infinite loop. This arises from newly introduced support for ellipsis in axis definition. An attacker can craft a model such that ellipsis_end_idx is smaller than i (e.g., always negative). In this case, the inner loop does not increase i and the continue statement causes execution to skip over the preincrement at the end of the outer loop. We have patched the issue in GitHub commit dfa22b348b70bb89d6d6ec0ff53973bacb4f4695. TensorFlow 2.6.0 is the only affected version.

Impact

The strided slice implementation in TFLite has a logic bug which can allow an attacker to trigger an infinite loop. This arises from newly introduced support for ellipsis in axis definition:

  for (int i = 0; i < effective_dims;) {
    if ((1 << i) & op_context->params->ellipsis_mask) {
      // ...
      int ellipsis_end_idx =
          std::min(i + 1 + num_add_axis + op_context->input_dims - begin_count,
                   effective_dims);
      // ...
      for (; i < ellipsis_end_idx; ++i) {
        // ...
      }
      continue;
    }
    // ...
    ++i;
  }

An attacker can craft a model such that ellipsis_end_idx is smaller than i (e.g., always negative). In this case, the inner loop does not increase i and the continue statement causes execution to skip over the preincrement at the end of the outer loop.

Patches

We have patched the issue in GitHub commit dfa22b348b70bb89d6d6ec0ff53973bacb4f4695.

The fix will be included in TensorFlow 2.6.0. This is the only affected version.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by members of the Aivul Team from Qihoo 360.