Heap buffer overflow in `UnsortedSegmentSum` in TensorFlow

The buffer overflow in UnsortedSegmentSum stems from the truncation of datasize and numsegments fields from int64 to int32, which can result in negative values. These negative values then cause the program to attempt to access memory outside of the allocated heap buffer. While the vulnerability is considered unlikely to be directly exploitable, successful exploitation could lead to a denial-of-service (DoS) condition or, in a more complex scenario, potentially allow for arbitrary code execution. The internal detection and fix indicate a low risk of active exploitation, but updating remains a best practice.

Organizations and individuals using TensorFlow versions 1.9.0 and earlier, particularly those deploying TensorFlow in production environments or processing sensitive data, are at risk. Systems with custom TensorFlow builds or integrations should be carefully reviewed to ensure they are not vulnerable.

• python / tensorflow: Inspect TensorFlow code for usage of UnsortedSegmentSum with potentially truncated int64 inputs. Monitor TensorFlow logs for memory access errors or crashes.

import tensorflow as tf
# Example: Check if data_size and num_segments are within reasonable bounds
data_size = tf.constant(..., dtype=tf.int64)
num_segments = tf.constant(..., dtype=tf.int64)
if data_size < 0 or num_segments < 0:
    print("Potential vulnerability: Negative data size or num_segments")

1. 2019-12-16Disclosure

   disclosure

1. Gereserveerd2019-09-24
2. Gepubliceerd2019-12-16
3. Gewijzigd2026-03-13
4. EPSS bijgewerkt2026-04-02

The primary mitigation for CVE-2019-16778 is to upgrade to TensorFlow version 1.15.0 or later, where the vulnerability has been addressed. If upgrading is not immediately feasible, consider implementing runtime checks to validate the sizes of datasize and numsegments before passing them to UnsortedSegmentSum. While a WAF or proxy is unlikely to directly mitigate this vulnerability, ensuring the TensorFlow environment is isolated and monitored can help detect any anomalous behavior. After upgrading, confirm the fix by running TensorFlow code that utilizes UnsortedSegmentSum and verifying that no out-of-bounds memory access errors occur.