CVE-2022-29201 — MEDIUM (CVSS 5.5) AI Security Vulnerability

CISO Take

A null pointer dereference in TensorFlow's QuantizedConv2D op allows any local user with low privileges to crash the TF runtime by passing empty input tensors, causing a denial of service. Patch immediately to TF 2.9.0, 2.8.1, 2.7.2, or 2.6.4 — this is a one-line exploit against unpatched inference servers. Risk is contained to availability; no data exfiltration or code execution confirmed.

Risk Assessment

MEDIUM risk. CVSS 5.5 with local attack vector limits blast radius, but in multi-tenant ML inference environments (Jupyter hubs, shared training clusters, model serving APIs that expose raw TF ops) the effective attack surface widens significantly. No active exploitation in the wild and not in CISA KEV. The low attack complexity means any authenticated cluster user can weaponize this trivially once the CVE is public. Quantized models are common in edge/embedded and mobile deployments, broadening the exposure surface beyond data centers.

Affected Systems

Package	Ecosystem	Vulnerable Range	Patched
tensorflow	pip	—	No patch
195.0K OpenSSF 7.2 3.7K dependents Pushed 6d ago 4% patched ~1372d to patch Full package profile →

Do you use tensorflow? You're affected.

Severity & Risk

CVSS 3.1

5.5 / 10

EPSS

0.1%

chance of exploitation in 30 days

Higher than 17% of all CVEs

Source: EPSS v3 — FIRST.org

Exploitation Status

Exploit Available

Exploitation: MEDIUM

Sophistication

Trivial

Exploitation Confidence

medium

○ CISA SSVC: Public PoC

○ Public PoC indexed (trickest/cve)

Composite signal derived from CISA KEV, CISA SSVC, EPSS, trickest/cve, and Nuclei templates.

Attack Surface

AV Local

AC Low

PR Low

UI None

S Unchanged

C None

I None

A High

Recommended Action

5 steps

Patch: Upgrade TensorFlow to >=2.9.0, >=2.8.1, >=2.7.2, or >=2.6.4 immediately.
Workaround if patching is blocked: Add input validation middleware to reject requests with empty or zero-dimension tensors before they reach TF ops.
Detection: Monitor for unexpected TF process crashes or SIGSEGV/SIGABRT signals in model serving logs; alert on repeated process restarts in serving infrastructure.
Restrict access: Enforce least privilege on who can submit inference requests to endpoints exposing raw TF ops — prefer typed APIs over passthrough op execution.
Inventory: Audit whether any model in production uses QuantizedConv2D ops and prioritize those services for patching.

CISA SSVC Assessment

Decision Track*

Exploitation poc

Automatable No

Technical Impact partial

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

Classification

DoS Framework Inference AML.T0010.001 - AI Software AML.T0029 - Denial of AI Service AML.T0043 - Craft Adversarial Data AML.T0049 - Exploit Public-Facing Application

Compliance Impact

This CVE is relevant to:

EU AI Act

Article 15 - Accuracy, robustness and cybersecurity

ISO 42001

A.9.3 - Technical testing of AI systems

NIST AI RMF

GOVERN 6.2 - Policies and procedures for AI risk management are in place MANAGE 2.4 - Residual risks from AI system operation are monitored and managed

Frequently Asked Questions

What is CVE-2022-29201?

A null pointer dereference in TensorFlow's QuantizedConv2D op allows any local user with low privileges to crash the TF runtime by passing empty input tensors, causing a denial of service. Patch immediately to TF 2.9.0, 2.8.1, 2.7.2, or 2.6.4 — this is a one-line exploit against unpatched inference servers. Risk is contained to availability; no data exfiltration or code execution confirmed.

Is CVE-2022-29201 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2022-29201, increasing the risk of exploitation.

How to fix CVE-2022-29201?

1. Patch: Upgrade TensorFlow to >=2.9.0, >=2.8.1, >=2.7.2, or >=2.6.4 immediately. 2. Workaround if patching is blocked: Add input validation middleware to reject requests with empty or zero-dimension tensors before they reach TF ops. 3. Detection: Monitor for unexpected TF process crashes or SIGSEGV/SIGABRT signals in model serving logs; alert on repeated process restarts in serving infrastructure. 4. Restrict access: Enforce least privilege on who can submit inference requests to endpoints exposing raw TF ops — prefer typed APIs over passthrough op execution. 5. Inventory: Audit whether any model in production uses QuantizedConv2D ops and prioritize those services for patching.

What systems are affected by CVE-2022-29201?

This vulnerability affects the following AI/ML architecture patterns: model serving, training pipelines, edge/embedded inference, quantization-aware training pipelines, multi-tenant ML platforms.

What is the CVSS score for CVE-2022-29201?

CVE-2022-29201 has a CVSS v3.1 base score of 5.5 (MEDIUM). The EPSS exploitation probability is 0.06%.

Technical Details

NVD Description

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.QuantizedConv2D` does not fully validate the input arguments. In this case, references get bound to `nullptr` for each argument that is empty. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

Exploitation Scenario

An adversary with a valid account on a shared ML platform (e.g., internal Jupyter hub or multi-tenant KServe deployment) identifies that a production model uses quantized convolution layers. They craft an inference request passing an empty tensor as the filter or input argument to tf.raw_ops.QuantizedConv2D. TensorFlow fails to validate the argument, binds a nullptr, and dereferences it during kernel execution, causing an immediate process crash. In a model serving context without auto-restart, the endpoint becomes unavailable. With auto-restart and no rate limiting, the attacker replays the request continuously to maintain a denial-of-service condition against all models sharing that serving instance.