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CVE-2026-56671 – Unauthenticated Path Traversal / Arbitrary File Read – ComfyUI before 0.28.0

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Summary

CVE-2026-56671 is an unauthenticated path traversal vulnerability in ComfyUI, the modular diffusion-model GUI, API, and backend from Comfy-Org. The flaw resides in the get_model_preview function and allows a remote attacker to read arbitrary image-decodable files and enumerate host paths without authentication. It affects all ComfyUI versions prior to 0.28.0 and is rated High (CVSS 7.5).

Technical details

  • Root cause: The get_model_preview function in app/model_manager.py joins an unrestricted, route-captured filename to a base directory using os.path.join(folder, filename) without a containment check (CWE-22, Improper Limitation of a Pathname to a Restricted Directory).
  • Trigger conditions: A request to the /experiment/models/preview endpoint supplying a crafted filename. Bypass techniques include literal directory traversal sequences, percent-encoded equivalents, and absolute paths; the path_index parameter is used without bounds checking.
  • Attack vector: Network, no authentication and no user interaction required.
  • Impact: Disclosure of confidentiality only — an attacker can read image-decodable files from arbitrary filesystem locations and enumerate host directory paths. No integrity or availability impact.

Affected software

  • ComfyUI (Comfy-Org) — all versions prior to 0.28.0.

Severity

  • CVSS v3.1 Base Score: 7.5 (High)
  • Vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N

Mitigation and recommended actions

  • Immediate: Upgrade to ComfyUI 0.28.0 or later, which resolves the vulnerability.
  • If no patch: Restrict network access to the ComfyUI instance — do not expose it directly to the internet; place it behind authentication, a reverse proxy, or a VPN, and block untrusted access to the /experiment/models/preview endpoint.

How IONIX identifies potentially affected assets

IONIX matches the following signals against data already collected when it crawled the asset; identifying the technology sends no request beyond that crawl.

  • Raw response body: aria-label="Loading ComfyUI"
  • Favicon fingerprint: 316510273, 1554526046

References

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1

Map your entire attack surface (continously)

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2

Monitor for new CVEs

Dozens of threat intel feeds using agentic technology are continuously analyzed to detect the appearance of proof-of-concept code, exploit kits, and indicators of active targeting. IONIX goes further by applying AI to proactively evaluate whether emerging vulnerabilities are likely to be exploited, even before PoCs go public.

3

Identify Potential External Exposures

Not all CVEs matter. IONIX filters vulnerabilities by asking attacker-centric questions: Can it be reached from the internet? Does it require authentication? Is it being exploited in the wild? This dramatically reduces noise and focuses teams on threats that can actually be weaponized.

4

Create Safe, Scalable Exploit Validations

IONIX transforms real-world PoCs into safe, non-intrusive test payloads that can be run in production environments without disruption. These simulations are precisely targeted to the systems that are vulnerable, ensuring rapid validation without unnecessary load.

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By combining context about software stack, versioning, exposure status, and reachability, IONIX ensures that only the right payloads are executed against the right assets, maximizing efficiency and minimizing risk.

6

Drive Fast and Actionable Remediation

Results are routed through integrations with ticketing, SOAR, and SIEM tools. Issues are written in plain language, bundled into remediation clusters, and prioritized based on asset criticality, exploitability, and blast radius. This shortens mean time to remediation (MTTR) and empowers teams to act with confidence.

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