Cyber Resilience

CVE-2024-4099

Gitlab 16.0.0 – 17.2.8

Published
26 September 2024
Modified
04 October 2024
CVSS Score v3.1 3.1
Click a component to see what it means
Raw vectorCVSS:3.1/AV:N/AC:H/PR:N/UI:R/S:U/C:N/I:L/A:N
EPSS Score 0.0028 20th percentile
Risk Priority 15 floored blend · peak EPSS

Summary

CVE-2024-4099 is a low-severity Improper Encoding or Escaping of Output (CWE-116) vulnerability in Gitlab Gitlab. Its CVSS base score is 3.1 (Low).

Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 20th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.

This vulnerability is AI-related — categorised as Enterprise AI Assistants; in the LLM/Generative AI Risks risk domain; MITRE ATLAS techniques in scope: LLM Prompt Injection (AML.T0051).

OWASP Top 10 for Web (2025)

A05:2025 Injection

EU & UK References

Vulnerability Data

An issue has been discovered in GitLab EE affecting all versions starting from 16.0 prior to 17.2.8, from 17.3 prior to 17.3.4, and from 17.4 prior to 17.4.1. An AI feature was found to read unsanitized content in a way…

more

that could have allowed an attacker to hide prompt injection.

CWE(s)
CWE-116

AI Security AnalysisAI

AI Category
Enterprise AI Assistants
Risk Domain
LLM/Generative AI Risks
OWASP Top 10 for LLMs 2025
None mapped
AI-specific weaknesses CR
CWE-1427
  • CWE-1427 — Unsanitized input to AI feature enables hidden prompt injection (CWE-1427).
Mapped by Cyber Resilience · not in NVD. Poisoning and extraction cases are routed to MITRE ATLAS instead of a synthetic CWE.
Classification Reason
The vulnerability affects an AI feature in GitLab EE, which includes enterprise AI assistants like GitLab Duo for code assistance, making it fit the Enterprise AI Assistants category.

Related Threats

MITRE ATT&CK Enterprise TechniquesAI

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.
attack.mitre.org →
T1213.003 Code Repositories Collection
Adversaries may leverage code repositories to collect valuable information.
attack.mitre.org →
Why these techniques?

Vulnerability in GitLab EE AI feature enables exploitation of public-facing application (T1190) via unsanitized content allowing hidden prompt injection; facilitates data collection from code repositories (T1213.003) by manipulating AI to reveal project/repo contents.

MITRE ATLAS TechniquesAI

MITRE ATLAS techniques

AML.T0051: LLM Prompt Injection

CVEs Like This One

CVE-2024-3303Same product: Gitlab Gitlab
CVE-2024-6329Same product: Gitlab Gitlab
CVE-2025-8405Same product: Gitlab Gitlab
CVE-2025-12697Same product: Gitlab Gitlab
CVE-2026-6713Same product: Gitlab Gitlab
CVE-2025-9957Same product: Gitlab Gitlab
CVE-2026-2104Same product: Gitlab Gitlab
CVE-2025-2867Same product: Gitlab Gitlab
CVE-2025-13772Same product: Gitlab Gitlab
CVE-2026-3553Same product: Gitlab Gitlab

Affected Assets

gitlab
gitlab
17.4.0 · 16.0.0 — 17.2.8 · 17.3.0 — 17.3.4

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)
  • V1.1.2
  • V1.2.1
  • V1.2.3

Likely Mitigating Controls AI

Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.

SI-15 Information Output Filtering
addresses: CWE-116

Validating that output matches expected content directly mitigates failures to properly encode or escape data for its destination context.

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 proper output encoding to prevent injection and message malformation.

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.

A.8.28 Secure coding mostly match
prevents

Secure coding standards explicitly require correct output encoding and escaping to preserve message structure.

A.8.29 Security testing in development and acceptance mostly match
detects

Security testing can detect missing or incorrect encoding but does not itself implement the control.

A.8.25 Secure development life cycle partial match
prevents

Secure development life cycle mandates output encoding/escaping practices that directly prevent improper encoding.

A.8.26 Application security requirements partial match
prevents

Application security requirements include explicit rules for safe output handling and encoding.

A.8.27 Secure system architecture and engineering principles partial match
prevents

Secure architecture principles reduce the likelihood of missing encoding but do not prescribe the actual technique.

References