Cyber Resilience

CVE-2026-7669

Published
02 May 2026
Modified
05 May 2026
CVSS Score v4 6.3
Click a component to see what it means
Raw vectorCVSS:4.0/AV:N/AC:H/AT:N/PR:N/UI:N/VC:L/VI:L/VA:L/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X
EPSS Score 0.0037 30th percentile
Risk Priority 31 floored blend · peak EPSS

Summary

CVE-2026-7669 is a medium-severity Injection (CWE-74) vulnerability. Its CVSS base score is 6.3 (Medium).

Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 30th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.

This vulnerability is AI-related — categorised as NLP and Transformers; in the Supply Chain and Deployment risk domain.

The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SI-10 (Information Input Validation) — see the control section below for these in your framework.

OWASP Top 10 for Web (2025)

EU & UK References

Vulnerability Data

A vulnerability was detected in sgl-project SGLang up to 0.5.9. Impacted is the function get_tokenizer of the file python/sglang/srt/utils/hf_transformers_utils.py of the component HuggingFace Transformer Handler. The manipulation of the argument trust_remote_code with the input False as part of Boolean results…

more

in code injection. The attack can be executed remotely. A high complexity level is associated with this attack. The exploitability is considered difficult. In get_tokenizer(), when the caller passes trust_remote_code=False and HuggingFace transformers v5 returns a TokenizersBackend instance (the generic fallback for tokenizer classes not in the registry), SGLang silently re-invokes AutoTokenizer.from_pretrained with trust_remote_code=True, overriding the caller's explicit security setting. A model repository containing a malicious tokenizer.py referenced via auto_map in tokenizer_config.json will execute arbitrary Python in the SGLang process during this second call. No log line or warning is emitted. The override affects all current SGLang versions because transformers==5.3.0 is pinned in pyproject.toml. Both tokenizer_mode="auto" and tokenizer_mode="slow" are affected. The exploit is now public and may be used. The vendor was contacted early about this disclosure but did not respond in any way.

CWE(s)

AI Security AnalysisAI

AI Category
NLP and Transformers
Risk Domain
Supply Chain and Deployment
OWASP Top 10 for LLMs 2025
None mapped
Classification Reason
Matched keywords: huggingface, transformers

Related Threats

MITRE ATT&CK Enterprise Techniques

T1190 Exploit Public-Facing Application Initial Access
Adversaries may attempt to exploit a weakness in an Internet-facing host or system to initially access a network.
T1221 Template Injection Stealth
Adversaries may create or modify references in user document templates to conceal malicious code or force authentication attempts.
T1659 Content Injection Initial Access
Adversaries may gain access and continuously communicate with victims by injecting malicious content into systems through online network traffic.
T1674 Input Injection Execution
Adversaries may simulate keystrokes on a victim’s computer by various means to perform any type of action on behalf of the user, such as launching the command interpreter using keyboard shortcuts, typing an inline script to be executed,…
T1059 Command and Scripting Interpreter Execution
Adversaries may abuse command and script interpreters to execute commands, scripts, or binaries.
T1059.001 PowerShell Execution
Adversaries may abuse PowerShell commands and scripts for execution.
Derived from this CVE’s CWE(s) via the direct CWE→ATT&CK cross-walk.

CVEs Like This One

CVE-2026-4998Shared CWE-74, CWE-94
CVE-2025-12266Shared CWE-74, CWE-94
CVE-2026-6125Shared CWE-74, CWE-94
CVE-2026-5562Shared CWE-74, CWE-94
CVE-2026-71320Shared CWE-74, CWE-94
CVE-2026-7595Shared CWE-74, CWE-94
CVE-2026-7703Shared CWE-74, CWE-94
CVE-2025-3563Shared CWE-74, CWE-94
CVE-2024-13187Shared CWE-74, CWE-94
CVE-2026-4515Shared CWE-74, CWE-94

Affected Assets

SGLang
inferred from references and description; NVD did not file a CPE for this CVE

Mitigating Controls

Control response

Prevent
Stop it (NIST 800-53)

Detect
Catch it (NIST detect / respond)

Harden
Shrink the surface (DISA STIG)

Validate
Prove the fix (OWASP ASVS)
  • V1.2.1
  • V1.2.3
  • V1.2.5
  • V1.2.8

Mitigating Controls (NIST 800-53 r5) AI

Developer testing and evaluation finds code paths that accept and execute externally influenced strings.

SI-10 directly requires validation of information inputs to reject malformed or special-element content before it reaches downstream parsers.

Least privilege limits the damage an injected code fragment can perform once executed.

Requiring documented secure development standards and tools enforces use of safe code-generation APIs and escaping.

Mitigating Controls (NIST CSF 2.0) AI

Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.

PR.PS-06 mostly match
prevents

Secure SDLC practices directly require input validation and output encoding that prevent injection flaws.

PR.DS-10 none match
prevents

PR.DS-10 protects runtime data confidentiality/integrity but has no bearing on neutralizing externally influenced input during code generation, so neither direction shows any preventive effect.

Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI

Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.

finds

Security testing in development catches injection vulnerabilities before release.

A.8.15 Logging partial match
finds

Logging supports detection of injection attempts but does not prevent the weakness.

finds

Monitoring activities can identify active injection attacks after they occur.

prevents

Secure development life cycle mandates input validation and output encoding that directly prevent injection flaws.

prevents

Application security requirements explicitly call for controls against injection attacks in software design.

prevents

Secure architecture principles reduce injection surfaces but do not prescribe specific neutralization techniques.

References