Raw vector
CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:H/VI:N/VA:N/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:XCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2026-22664 is a high-severity SSRF (CWE-918) vulnerability in Fka Prompts.Chat. Its CVSS base score is 7.1 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 22th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
This vulnerability is AI-related — categorised as LLM Application Platforms; in the Privacy and Disclosure risk domain.
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.
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-2026-22664 is a server-side request forgery (SSRF) vulnerability, mapped to CWE-918, affecting prompts.chat prior to commit 30a8f04. The flaw exists in the Fal.ai media status polling feature, where a lack of URL validation in the token parameter enables authenticated users to trigger arbitrary outbound requests from the server.
Attackers with low privileges (PR:L) can exploit this over the network (AV:N) with low complexity (AC:L) and no user interaction (UI:N), achieving high confidentiality impact (C:H) in a changed scope (S:C) but no integrity or availability effects. By supplying attacker-controlled URLs, they can force the server to make requests that disclose the FAL_API_KEY in the Authorization header, resulting in credential theft, internal network probing, and abuse of the victim's Fal.ai account. The CVSS v3.1 base score is 7.7.
Mitigation involves updating to commit 30a8f0470e0ba45e6be9c9f55220f4a9a6b91c99 or later in the prompts.chat repository, which addresses the URL validation issue. Detailed analysis is provided in VulnCheck's advisory at https://www.vulncheck.com/advisories/prompts-chat-ssrf-via-fal-ai-media-status-polling and researcher mdisec's GitHub Gist at https://gist.github.com/mdisec/27c0cac0ec6a8f3c8f85a18987ddb942.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-18827
Vulnerability Data
prompts.chat prior to commit 30a8f04 contains a server-side request forgery vulnerability in the Fal.ai media status polling feature that allows authenticated users to perform arbitrary outbound requests by supplying attacker-controlled URLs in the token parameter. Attackers can exploit the lack…
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of URL validation to disclose the FAL_API_KEY in the Authorization header, enabling credential theft, internal network probing, and abuse of the victim's Fal.ai account.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- Privacy and Disclosure
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: ai
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.