CVE-2023-25662: TensorFlow integer overflow

CISO Take

Any TensorFlow deployment exposing inference APIs to untrusted inputs is vulnerable to remote process crash—no authentication required. Upgrade to TensorFlow 2.12.0 or 2.11.1 immediately. Production ML services must patch before deploying; add input shape validation at the API gateway as a short-term compensating control.

What is the risk?

HIGH risk for internet-exposed TensorFlow inference services. CVSS 7.5 with network vector, low complexity, no privileges, and no user interaction makes this trivially exploitable. Impact is purely availability (A:H)—no data exfiltration or code execution path exists. For batch training pipelines isolated from untrusted inputs, risk drops to LOW. Not in CISA KEV, suggesting no confirmed active exploitation at time of publication, but low exploitation barrier warrants priority patching.

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 31% of all CVEs

Source: EPSS v3 — FIRST.org

Exploitation Status

No known exploitation

Sophistication

Trivial

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

Upgrade TensorFlow to 2.12.0 (stable) or 2.11.1 (security branch) immediately.
WORKAROUND

Validate and enforce bounds on input tensor shapes and sequence lengths at the API gateway before forwarding to TensorFlow operations.
ISOLATION

Run TensorFlow inference workers in isolated containers with automatic restart policies to minimize downtime from triggered crashes.
DETECTION

Alert on abnormal TensorFlow process crashes or OOM errors correlated with unusual input sizes or request patterns from specific source IPs.
AUDIT

Enumerate all internal services using EditDistance and prioritize patching by exposure level (internet-facing first).

What does CISA's SSVC say?

Decision Track

Exploitation none

Automatable Yes

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 - AI System Design and Implementation

NIST AI RMF

MANAGE 2.2 - Mechanisms to Sustain AI System Value and Reduce Negative Impacts

OWASP LLM Top 10

LLM10:2025 - Unbounded Consumption

Frequently Asked Questions

What is CVE-2023-25662?

Any TensorFlow deployment exposing inference APIs to untrusted inputs is vulnerable to remote process crash—no authentication required. Upgrade to TensorFlow 2.12.0 or 2.11.1 immediately. Production ML services must patch before deploying; add input shape validation at the API gateway as a short-term compensating control.

Is CVE-2023-25662 actively exploited?

No confirmed active exploitation of CVE-2023-25662 has been reported, but organizations should still patch proactively.

How to fix CVE-2023-25662?

1. PATCH: Upgrade TensorFlow to 2.12.0 (stable) or 2.11.1 (security branch) immediately. 2. WORKAROUND: Validate and enforce bounds on input tensor shapes and sequence lengths at the API gateway before forwarding to TensorFlow operations. 3. ISOLATION: Run TensorFlow inference workers in isolated containers with automatic restart policies to minimize downtime from triggered crashes. 4. DETECTION: Alert on abnormal TensorFlow process crashes or OOM errors correlated with unusual input sizes or request patterns from specific source IPs. 5. AUDIT: Enumerate all internal services using EditDistance and prioritize patching by exposure level (internet-facing first).

What systems are affected by CVE-2023-25662?

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

What is the CVSS score for CVE-2023-25662?

CVE-2023-25662 has a CVSS v3.1 base score of 7.5 (HIGH). The EPSS exploitation probability is 0.39%.

What is the AI security impact?

Affected AI Architectures

model servingtraining pipelinesML inference APIs

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

NIST AI RMF: MANAGE 2.2

OWASP LLM Top 10: LLM10:2025

What are the technical details?

Original Advisory

TensorFlow is an open source platform for machine learning. Versions prior to 2.12.0 and 2.11.1 are vulnerable to integer overflow in EditDistance. A fix is included in TensorFlow version 2.12.0 and version 2.11.1.

Exploitation Scenario

An adversary identifies a public-facing ML inference API built on unpatched TensorFlow—for example, a text similarity scoring or speech recognition service. They craft an HTTP POST request containing tensor inputs with values engineered to trigger an integer overflow in the EditDistance computation, causing the TF worker process to crash and return a 500 error. By automating requests at regular intervals, the attacker sustains a persistent DoS condition against production inference endpoints, breaching SLA commitments and disrupting compliance-critical AI applications without needing any credentials or prior access.

Weaknesses (CWE)

CWE-190 Integer Overflow or Wraparound

CWE-190 — Integer Overflow or Wraparound: The product performs a calculation that can produce an integer overflow or wraparound when the logic assumes that the resulting value will always be larger than the original value. This occurs when an integer value is incremented to a value that is too large to store in the associated representation. When this occurs, the value may become a very small or negative number.

[Requirements] Ensure that all protocols are strictly defined, such that all out-of-bounds behavior can be identified simply, and require strict conformance to the protocol.
[Requirements] Use a language that does not allow this weakness to occur or provides constructs that make this weakness easier to avoid. If possible, choose a language or compiler that performs automatic bounds checking.

Source: MITRE CWE corpus.