CVE intelligence and bounded remediation
CVE-2022-35971 — TensorFlow is an open source platform for machine learning
TensorFlow is an open source platform for machine learning. If `FakeQuantWithMinMaxVars` is given `min` or `max` tensors of a nonzero rank, it results in a `CHECK` fail that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 785d67a78a1d533759fcd2f5e8d6ef778de849e0. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.
- Severity
- High
- CVSS
- 7.5 (3.1)
- Published
- 2022-09-16
- CISA KEV
- Not currently listed
- Ecosystem
- software/application
- Weaknesses
- CWE-617
Affected products
- google / tensorflow
- google / tensorflow / 2.10
Matched remediation archetype
Resource exhaustion and denial of service
This catalog composition supplies bounded fallback guidance. Explicitly reviewed curated workflows load with the complete record below.
Check exposure
- Identify attacker-influenced work factors including input size, nesting, compression, fan-out, regex cost, allocation, recursion, retries, and connection lifetime.
- Map per-request and shared CPU, memory, disk, descriptor, thread, queue, and downstream-service limits.
- Determine whether authentication, tenancy, quotas, and rate controls apply before expensive processing begins.
Remediate safely
- Bound input size, nesting, expansion, work, concurrency, queue depth, retries, and execution time before resource-intensive processing.
- Release resources on every success, error, cancellation, and timeout path and use backpressure instead of unbounded buffering.
- Update affected components and add small deterministic tests that assert resource ceilings rather than exhausting a host.
Authoritative sources
Complete CVE record and remediation plan
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