CVE-2026-22777
Comfyui-Manager ≤ 3.39.2
Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:H/A:NSummary
CVE-2026-22777 is a high-severity CRLF Injection (CWE-93) vulnerability in Comfy Comfyui-Manager. Its CVSS base score is 7.5 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Content Injection (T1659); ranked at the 24th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as LLM Application Platforms; in the Supply Chain and Deployment risk domain.
The strongest mitigations our analysis identified map to 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-2026-22777 is a vulnerability in ComfyUI-Manager, an extension designed to enhance the usability of ComfyUI, affecting versions prior to 3.39.2 and 4.0.5. It enables an attacker to inject special characters into HTTP query parameters, allowing the addition of arbitrary configuration values to the config.ini file. This can lead to security setting tampering or modification of application behavior. The issue, published on 2026-01-10, is associated with CWE-93 and carries a CVSS v3.1 base score of 7.5 (AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:H/A:N), emphasizing high integrity impact.
The vulnerability can be exploited by any remote attacker with network access, requiring low attack complexity, no privileges, and no user interaction. Successful exploitation allows modification of the config.ini file, enabling tampering with security settings or altering application behavior to the attacker's advantage, without impacting confidentiality or availability.
Mitigation is available through patching: upgrade to ComfyUI-Manager versions 3.39.2 or 4.0.5. Detailed advisory information and the patching commit are provided in the GitHub security advisory (https://github.com/Comfy-Org/ComfyUI-Manager/security/advisories/GHSA-562r-8445-54r2) and commit (https://github.com/Comfy-Org/ComfyUI-Manager/commit/f4fa394e0f03b013f1068c96cff168ad10bd0410).
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-1864
Vulnerability Data
ComfyUI-Manager is an extension designed to enhance the usability of ComfyUI. Prior to versions 3.39.2 and 4.0.5, an attacker can inject special characters into HTTP query parameters to add arbitrary configuration values to the config.ini file. This can lead to…
more
security setting tampering or modification of application behavior. This issue has been patched in versions 3.39.2 and 4.0.5.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- Supply Chain and Deployment
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: comfyui
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V4.2.4
Mitigating Controls (NIST 800-53 r5) AI
Input validation directly stops untrusted data containing CRLF sequences from reaching the component that treats CRLF as a delimiter.
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.
Secure SDLC practices directly require input neutralization and validation to block CRLF injection.
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.
Security testing can detect CRLF flaws but does not itself implement the neutralization.
Secure development lifecycle mandates input validation and output encoding that directly prevents CRLF injection.
Application security requirements include rules for neutralizing special characters such as CRLF in inputs.
Secure architecture principles encourage safe handling of untrusted data but do not prescribe specific CRLF controls.
Secure coding standards explicitly require neutralization of CRLF sequences, fully addressing this weakness.