CVE intelligence and bounded remediation
CVE-2020-15201 — Google Tensorflow security vulnerability
In Tensorflow before version 2.3.1, the `RaggedCountSparseOutput` implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the `splits` tensor generate a valid partitioning of the `values` tensor. Hence, the code is prone to heap buffer overflow. If `split_values` does not end with a value at least `num_values` then the `while` loop condition will trigger a read outside of the bounds of `split_values` once `batch_idx` grows too large. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.
- Severity
- Medium
- CVSS
- 6.8 (2.0)
- Published
- 2020-09-25
- CISA KEV
- Not currently listed
- Ecosystem
- software/application
- Weaknesses
- CWE-20, CWE-122, CWE-787
Affected products
- google / tensorflow / 2.3.0
Matched remediation archetype
Buffer bounds, memory safety, and memory corruption
This catalog composition supplies bounded fallback guidance. Explicitly reviewed curated workflows load with the complete record below.
Check exposure
- Identify affected native-code versions, build flags, architectures, parsers, codecs, drivers, and input paths in all shipped artifacts.
- Determine whether untrusted data reaches the affected routine and the process privilege, sandbox, and network exposure.
- Confirm statically linked, vendored, firmware, and platform-provided copies, not only package-manager records.
Remediate safely
- Apply the maintained upstream correction or replace the affected component, then rebuild every dependent artifact from clean inputs.
- Adopt bounds-checked interfaces, validated sizes and integer conversions, clear ownership, and memory-safe components where practical.
- Enable supported compiler and runtime hardening and add sanitized tests and fuzz regression seeds derived from non-weaponized fixtures.
Authoritative sources
Complete CVE record and remediation plan
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