CVE-2022-35974: TensorFlow DoS — HIGH

CISO Take

A remotely exploitable DoS in TensorFlow's quantization pipeline lets an unauthenticated attacker crash any inference service running models with the QuantizeDownAndShrinkRange op. No authentication, no user interaction, network-accessible — CVSS 7.5. Patch to TensorFlow 2.10.0, 2.9.1, 2.8.1, or 2.7.2 immediately; no workaround exists.

What is the risk?

HIGH risk for organizations running public-facing TensorFlow inference services with quantized models. Zero prerequisites make this trivially exploitable: any client submitting inference requests can trigger a segfault and crash the serving process. Impact is availability-only (no data exfiltration risk), but automated restart loops can be exploited to maintain sustained outages. Internal training pipelines that ingest external data are also exposed.

What systems are affected?

Package	Ecosystem	Vulnerable Range	Patched
TensorFlow	pip	—	No patch
195.8K OpenSSF 7.1 3.7K dependents Pushed 3d ago 4% patched ~1372d to patch Full package profile →

Do you use TensorFlow? You're affected.

How severe is it?

CVSS 3.1

7.5 / 10

EPSS

0.4%

chance of exploitation in 30 days

Higher than 32% of all CVEs

Source: EPSS v3 — FIRST.org

Exploitation Status

Exploit Available

Exploitation: MEDIUM

Sophistication

Trivial

Exploitation Confidence

medium

○ Public PoC indexed (trickest/cve)

Composite signal derived from CISA KEV, VulnCheck KEV, CISA SSVC, EPSS, Metasploit, Exploit-DB, trickest/cve, Nuclei templates, and inthewild.io exploitation reports.

What is the attack surface?

AV Network

AC Low

PR None

UI None

S Unchanged

C None

I None

A High

What should I do?

5 steps

Patch immediately: upgrade to TensorFlow 2.10.0, 2.9.1, 2.8.1, or 2.7.2 (fix: commit 73ad1815).
If patching is delayed: enforce server-side scalar validation on input_min and input_max before passing tensors to TF ops.
Deploy input sanitization / WAF in front of all model serving endpoints.
Configure liveness probes and process supervisors with backoff to limit restart-loop exploitation.
Monitor serving infrastructure for unexpected process crashes or SIGSEGV signals as indicators of exploitation.

What does CISA's SSVC say?

Decision Track

Exploitation none

Automatable No

Technical Impact partial

Source: CISA Vulnrichment (SSVC v2.0). Decision based on the CISA Coordinator decision tree.

How is it classified?

DoS Framework Inference AML.T0010.001 - AI Software AML.T0029 - Denial of AI Service AML.T0049 - Exploit Public-Facing Application

Which compliance frameworks are affected?

This CVE is relevant to:

EU AI Act

Article 15 - Accuracy, robustness and cybersecurity

ISO 42001

A.6.2.5 - AI system availability and resilience

NIST AI RMF

MS-2.5 - Manage risks related to AI system failures and vulnerabilities

OWASP LLM Top 10

LLM04 - Model Denial of Service

Frequently Asked Questions

What is CVE-2022-35974?

A remotely exploitable DoS in TensorFlow's quantization pipeline lets an unauthenticated attacker crash any inference service running models with the QuantizeDownAndShrinkRange op. No authentication, no user interaction, network-accessible — CVSS 7.5. Patch to TensorFlow 2.10.0, 2.9.1, 2.8.1, or 2.7.2 immediately; no workaround exists.

Is CVE-2022-35974 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2022-35974, increasing the risk of exploitation.

How to fix CVE-2022-35974?

1. Patch immediately: upgrade to TensorFlow 2.10.0, 2.9.1, 2.8.1, or 2.7.2 (fix: commit 73ad1815). 2. If patching is delayed: enforce server-side scalar validation on input_min and input_max before passing tensors to TF ops. 3. Deploy input sanitization / WAF in front of all model serving endpoints. 4. Configure liveness probes and process supervisors with backoff to limit restart-loop exploitation. 5. Monitor serving infrastructure for unexpected process crashes or SIGSEGV signals as indicators of exploitation.

What systems are affected by CVE-2022-35974?

This vulnerability affects the following AI/ML architecture patterns: model serving, inference pipelines, training pipelines.

What is the CVSS score for CVE-2022-35974?

CVE-2022-35974 has a CVSS v3.1 base score of 7.5 (HIGH). The EPSS exploitation probability is 0.40%.

What is the AI security impact?

Affected AI Architectures

model servinginference pipelinestraining pipelines

MITRE ATLAS Techniques

AML.T0010.001 AI Software

AML.T0029 Denial of AI Service

AML.T0049 Exploit Public-Facing Application

Compliance Controls Affected

EU AI Act: Article 15

ISO 42001: A.6.2.5

NIST AI RMF: MS-2.5

OWASP LLM Top 10: LLM04

What are the technical details?

Original Advisory

TensorFlow is an open source platform for machine learning. If `QuantizeDownAndShrinkRange` is given nonscalar inputs for `input_min` or `input_max`, it results in a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 73ad1815ebcfeb7c051f9c2f7ab5024380ca8613. 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.

Exploitation Scenario

Adversary identifies a public TensorFlow Serving endpoint (gRPC or REST) running a quantized model. Using standard TF Serving client libraries, they craft an inference request with nonscalar tensors for input_min or input_max — trivially constructed with numpy or tf.constant. The QuantizeDownAndShrinkRange op receives invalid input, segfaults, and crashes the serving process. No ML expertise required; basic input fuzzing surfaces this behavior. Under auto-restart configurations, the adversary loops requests to sustain a prolonged outage of the inference service.

Weaknesses (CWE)

CWE-20 Improper Input Validation

CWE-20 — Improper Input Validation: The product receives input or data, but it does not validate or incorrectly validates that the input has the properties that are required to process the data safely and correctly.

[Architecture and Design] Consider using language-theoretic security (LangSec) techniques that characterize inputs using a formal language and build "recognizers" for that language. This effectively requires parsing to be a distinct layer that effectively enforces a boundary between raw input and internal data representations, instead of allowing parser code to be scattered throughout the program, where it could be subject to errors or inconsistencies that create weaknesses. [REF-1109] [REF-1110] [REF-1111]
[Architecture and Design] Use an input validation framework such as Struts or the OWASP ESAPI Validation API. Note that using a framework does not automatically address all input validation problems; be mindful of weaknesses that could arise from misusing the framework itself (CWE-1173).

Source: MITRE CWE corpus.