Cyber Resilience

CVE-2025-2255

XSS in Gitlab 13.5.0 – 17.8.6

Public PoCXSS
Published
27 March 2025
Modified
13 August 2025
CVSS Score v3.1 8.7
Click a component to see what it means
Raw vectorCVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:H/I:H/A:N
EPSS Score 0.0029 21th percentile
Risk Priority 59 floored blend · peak EPSS

Summary

CVE-2025-2255 is a high-severity Cross-site Scripting (CWE-79) vulnerability in Gitlab Gitlab. Its CVSS base score is 8.7 (High).

Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked at the 21th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.

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.

Deeper analysis AI-assisted summary

Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.

CVE-2025-2255 is a cross-site scripting (XSS) vulnerability, classified under CWE-79, in the AppSec component of GitLab Enterprise Edition (EE) and Community Edition (CE). It affects all versions from 13.5.0 prior to 17.8.6, 17.9 prior to 17.9.3, and 17.10 prior to 17.10.1. The flaw stems from certain error messages that could enable XSS attacks.

With a CVSS v3.1 base score of 8.7 (AV:N/AC:L/PR:L/UI:R/S:C/C:H/I:H/A:N), the vulnerability can be exploited over the network by an attacker possessing low privileges. Exploitation requires user interaction and low attack complexity, but successful attacks change scope and result in high impacts to confidentiality and integrity, such as potential session theft or manipulation of user data in the victim's browser.

Mitigation requires upgrading to GitLab versions 17.8.6, 17.9.3, or 17.10.1 or later. Further details on the issue and resolution are documented in the GitLab issue tracker at https://gitlab.com/gitlab-org/gitlab/-/issues/524635 and the corresponding HackerOne report at https://hackerone.com/reports/2994150.

OWASP Top 10 for Web (2025)

EU & UK References

Vulnerability Data

An issue has been discovered in Gitlab EE/CE for AppSec affecting all versions from 13.5.0 before 17.8.6, 17.9 before 17.9.3, and 17.10 before 17.10.1. Certain error messages could allow Cross-Site Scripting attacks (XSS). for AppSec.

CWE(s)

Related Threats

MITRE ATT&CK Enterprise Techniques

T1185 Browser Session Hijacking Collection
Adversaries may take advantage of security vulnerabilities and inherent functionality in browser software to change content, modify user-behaviors, and intercept information as part of various browser session hijacking techniques.
T1539 Steal Web Session Cookie Credential Access
An adversary may steal web application or service session cookies and use them to gain access to web applications or Internet services as an authenticated user without needing credentials.
T1659 Content Injection Initial Access
Adversaries may gain access and continuously communicate with victims by injecting malicious content into systems through online network traffic.
T1189 Drive-by Compromise Initial Access
Adversaries may gain access to a system through a user visiting a website over the normal course of browsing.
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.
Derived from this CVE’s CWE(s) via the direct CWE→ATT&CK cross-walk.

CVEs Like This One

CVE-2023-2442Same product: Gitlab Gitlab
CVE-2023-2164Same product: Gitlab Gitlab
CVE-2023-1836Same product: Gitlab Gitlab
CVE-2022-1190Same product: Gitlab Gitlab
CVE-2023-2015Same product: Gitlab Gitlab
CVE-2020-13340Same product: Gitlab Gitlab
CVE-2022-1175Same product: Gitlab Gitlab
CVE-2023-6033Same product: Gitlab Gitlab
CVE-2022-2230Same product: Gitlab Gitlab
CVE-2023-3500Same product: Gitlab Gitlab

Affected Assets

gitlab
gitlab
17.10.0 · 13.5.0 — 17.8.6 · 13.5.0 — 17.8.6 · 17.9.0 — 17.9.3

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
  • V1.3.2

Mitigating Controls (NIST 800-53 r5) AI

Developer testing and evaluation can discover missing or incorrect input neutralization through targeted web-application tests.

Input validation directly enforces neutralization of untrusted data before it reaches web output generation.

Output filtering can catch or sanitize unneutralized script content before it is served to users.

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 target introduction of XSS via coding standards/testing (mostly), yet the single broad outcome leaves many specific neutralization vectors unaddressed (partial).

PR.PS-02 partial match
prevents

Patching and EOL replacement can remediate known XSS instances in libraries or frameworks (partial) but do nothing to enforce input neutralization in application code (none).

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

Secure-coding testing and automated code-analysis tools are applied to detect improper neutralization of script-related content during web-page generation.

prevents

Knowledge exchange on emerging attack techniques and patches reduces the likelihood that cross-site scripting flaws remain unaddressed in deployed applications.

prevents

Operational indicators of compromise for web-application attacks can be incorporated into WAF or input-filtering rules, lowering the likelihood that unsanitized data reaches the browser.

prevents

Requiring language-specific secure-coding standards and automated scanning during the SDLC catches missing output encoding or improper neutralization of untrusted data before the software reaches production.

prevents

Secure-coding standards, SAST scans and removal of insecure code samples together eliminate the failure to neutralize script content that produces cross-site scripting flaws.

none

Webpage malware scanning and block-listing of known malicious sites reduce the likelihood that reflected or stored script payloads reach a user’s browser.

References