CVE-2022-21731: Type confusion leading to segfault in Tensorflow

Impact The implementation of shape inference for ConcatV2 can be used to trigger a denial of service attack via a segfault caused by a type confusion:

python import tensorflow as tf

@tf.function def test(): y = tf.rawops.ConcatV2( values=[[1,2,3],[4,5,6]], axis = 0xb500005b) return y

test()

The axis argument is translated into concatdim in the ConcatShapeHelper helper function. Then, a value for minrank is computed based on concatdim. This is then used to validate that the values tensor has at least the required rank:

cc int64t concatdim; if (concatdimt->dtype() == DTINT32) { concatdim = staticcast<int64t>(concatdimt->flat<int32>()(0)); } else { concatdim = concatdimt->flat<int64t>()(0); }

// Minimum required number of dimensions. const int minrank = concatdim < 0 ? -concatdim : concatdim + 1;

// ... ShapeHandle input = c->input(endvalueindex - 1); TFRETURNIFERROR(c->WithRankAtLeast(input, minrank, &input));

However, WithRankAtLeast receives the lower bound as a 64-bits value and then compares it against the maximum 32-bits integer value that could be represented:

cc Status InferenceContext::WithRankAtLeast(ShapeHandle shape, int64t rank, ShapeHandle out) { if (rank > kint32max) { return errors::InvalidArgument("Rank cannot exceed kint32max"); } // ... }

Due to the fact that minrank is a 32-bits value and the value of axis, the rank argument is a negative value, so the error check is bypassed.

Patches We have patched the issue in GitHub commit 08d7b00c0a5a20926363849f611729f53f3ec022.

The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

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Attribution This vulnerability has been reported by Yu Tian of Qihoo 360 AIVul Team.