CVE-2022-29198

Missing validation causes denial of service via SparseTensorToCSRSparseMatrix

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Attribution

This vulnerability has been reported by Neophytos Christou from Secure Systems Lab at Brown University.

The implementation of tf.raw_ops.SparseTensorToCSRSparseMatrix does not fully validate the input arguments. This results in a CHECK-failure which can be used to trigger a denial of service attack:

import tensorflow as tf

indices = tf.constant(53, shape=[3], dtype=tf.int64)
values = tf.constant(0.554979503, shape=[218650], dtype=tf.float32)
dense_shape = tf.constant(53, shape=[3], dtype=tf.int64)
    
tf.raw_ops.SparseTensorToCSRSparseMatrix(
  indices=indices,
  values=values,
  dense_shape=dense_shape)

The code assumes dense_shape is a vector and indices is a matrix (as part of requirements for sparse tensors) but there is no validation for this:

    const Tensor& indices = ctx->input(0);
    const Tensor& values = ctx->input(1);
    const Tensor& dense_shape = ctx->input(2);
    const int rank = dense_shape.NumElements();
    OP_REQUIRES(ctx, rank == 2 || rank == 3,
                errors::InvalidArgument("SparseTensor must have rank 2 or 3; ",
                                        "but indices has rank: ", rank));
    auto dense_shape_vec = dense_shape.vec<int64_t>();
    // ...
    OP_REQUIRES_OK(
        ctx,
        coo_to_csr(batch_size, num_rows, indices.template matrix<int64_t>(),
                   batch_ptr.vec<int32>(), csr_row_ptr.vec<int32>(),
                   csr_col_ind.vec<int32>()));

The application does not adequately validate input before processing it, allowing unexpected values to reach sensitive code paths. Typical impact: varies by context: data corruption, logic bypass, or denial of service.

CVE-2022-29198 has a CVSS score of 5.5 (Medium). The vector is requires local access, low privileges required, and no user interaction. A CVSS score reflects the worst-case severity of the vulnerability, not your specific exposure. Whether this affects your application depends on whether the vulnerable code is present and reachable in your environment. A fixed version is available (2.6.4, 2.7.2, 2.8.1); upgrading removes the vulnerable code path.

Kodem intelligence

Already deployed Kodem?

We have patched the issue in GitHub commit ea50a40e84f6bff15a0912728e35b657548cef11.

The fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, as these are also affected and still in supported range.

1. What is CVE-2022-29198? CVE-2022-29198 is a medium-severity improper input validation vulnerability in tensorflow (pip), affecting versions < 2.6.4. It is fixed in 2.6.4, 2.7.2, 2.8.1. The application does not adequately validate input before processing it, allowing unexpected values to reach sensitive code paths.
2. How severe is CVE-2022-29198? CVE-2022-29198 has a CVSS score of 5.5 (Medium). This score reflects the worst-case severity of the vulnerability, not your specific exposure. Whether it represents real risk in your environment depends on whether the vulnerable code is present and reachable.
3. Which packages are affected by CVE-2022-29198?
   • tensorflow (pip) (versions < 2.6.4)
   • tensorflow-cpu (pip) (versions < 2.6.4)
   • tensorflow-gpu (pip) (versions < 2.6.4)
4. Is there a fix for CVE-2022-29198? Yes. CVE-2022-29198 is fixed in 2.6.4, 2.7.2, 2.8.1. Upgrade to this version or later.
5. Is CVE-2022-29198 exploitable, and should I be worried? Whether CVE-2022-29198 is exploitable in your environment depends on whether the vulnerable code is present and reachable. A CVSS score is a worst-case rating; it does not account for your specific deployment, configuration, or usage patterns. Kodem, an Intelligent Application Security platform, uses runtime intelligence to show which vulnerabilities actually execute in production, so you can focus on the ones that represent real risk. Get a demo
6. What actually determines whether CVE-2022-29198 is exploitable, and how bad it is? Exploitability and impact are not fixed properties of a CVE. They depend on runtime truth: whether the vulnerable code is present, reachable, and actually executes in your application. A high CVSS score on a dependency that never runs is not the same as real risk. Kodem, an Intelligent Application Security platform, uses runtime intelligence to reveal which vulnerabilities actually execute in production, so teams prioritize the ones that genuinely matter.
7. How do I fix CVE-2022-29198?
   • Upgrade tensorflow to 2.6.4 or later
   • Upgrade tensorflow to 2.7.2 or later
   • Upgrade tensorflow to 2.8.1 or later
   • Upgrade tensorflow-cpu to 2.6.4 or later
   • Upgrade tensorflow-cpu to 2.7.2 or later
   • Upgrade tensorflow-cpu to 2.8.1 or later
   • Upgrade tensorflow-gpu to 2.6.4 or later
   • Upgrade tensorflow-gpu to 2.7.2 or later
   • Upgrade tensorflow-gpu to 2.8.1 or later

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