Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:N/A:NSummary
CVE-2026-53755 is a high-severity SSRF (CWE-918) vulnerability in Kidocode Crawl4Ai. Its CVSS base score is 8.6 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 21th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as Other AI Platforms.
The strongest mitigations our analysis identified map to AC-4 (Information Flow Enforcement) 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
No EU or UK CSIRT advisories indexed for this CVE.
Vulnerability Data
Crawl4AI is an open-source LLM friendly web crawler & scraper. Prior to 0.8.9, the Docker API server applied its SSRF destination check to the crawl target URL only, not to the proxy address. An unauthenticated request could supply a proxy…
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pointing at an internal IP and route the browser through it, reaching internal services and cloud-metadata endpoints, while using a perfectly valid crawl URL. The Docker API is unauthenticated by default. /crawl, /crawl/stream, and /crawl/job accept a browser_config (and crawler_config). The following all feed Chromium's egress and were unchecked: browser_config.proxy_config.server, browser_config.proxy (deprecated field), crawler_config.proxy_config.server, and --proxy-server / --proxy-pac-url / --proxy-bypass-list / --host-resolver-rules flags in browser_config.extra_args. This vulnerability is fixed in 0.8.9.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Other AI Platforms
- Risk Domain
- N/A
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: llm
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.3.6V1.5.3V5.3.2V10.4.7
Mitigating Controls (NIST 800-53 r5) AI
Information flow enforcement can restrict which destinations the server is allowed to contact on behalf of users.
Input validation directly stops untrusted URLs from being accepted and fetched without destination checks.
Boundary protection limits the network reach of server-initiated requests even if SSRF occurs.
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 development practices directly include input validation and destination allow-listing that prevent SSRF.
Runtime monitoring of web applications and services can detect anomalous outbound requests indicative of SSRF.
Vulnerability identification processes can discover and record SSRF flaws in web applications.
Network segmentation and egress controls can limit the damage from successful SSRF requests.
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.
Operational threat data describing SSRF campaigns can be used to tighten outbound-request allow-lists and detection rules before attackers exploit them.