Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:N/A:NSummary
CVE-2026-40150 is a high-severity SSRF (CWE-918) vulnerability in Praison Praisonaiagents. Its CVSS base score is 7.7 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 19th 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 AI Agent Protocols and Integrations; in the LLM/Generative AI Risks 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-2026-40150 is a server-side request forgery vulnerability (CWE-918) in PraisonAIAgents, a multi-agent teams system. Prior to version 1.5.128, the web_crawl() function in praisonaiagents/tools/web_crawl_tools.py accepts arbitrary URLs supplied by AI agents without validation, including no scheme allowlisting, hostname or IP blocklisting, or private network checks prior to fetching content. This flaw, scored 7.7 under CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:N/A:N, enables unauthorized resource access.
An attacker with low privileges, such as a legitimate user or through prompt injection in previously crawled content, can exploit this over the network with low complexity and no user interaction. By providing malicious URLs to the AI agent, they can force it to fetch sensitive cloud metadata endpoints (e.g., on AWS IMDS), internal services, or local files via file:// schemes, resulting in high confidentiality impact through potential data exfiltration in a scoped environment.
The vulnerability is addressed in PraisonAIAgents version 1.5.128. Security advisories recommend updating to this patched release immediately. Additional details are available in the GitHub Security Advisory at https://github.com/MervinPraison/PraisonAI/security/advisories/GHSA-8f4v-xfm9-3244.
This issue highlights risks in AI agent frameworks where untrusted inputs from prompts can lead to SSRF, with relevance to AI/ML deployments relying on automated web crawling tools. No public evidence of real-world exploitation is reported as of the CVE publication on 2026-04-09.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-21170
Vulnerability Data
PraisonAIAgents is a multi-agent teams system. Prior to 1.5.128, the web_crawl() function in praisonaiagents/tools/web_crawl_tools.py accepts arbitrary URLs from AI agents with zero validation. No scheme allowlisting, hostname/IP blocklisting, or private network checks are applied before fetching. This allows an attacker…
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(or prompt injection in crawled content) to force the agent to fetch cloud metadata endpoints, internal services, or local files via file:// URLs. This vulnerability is fixed in 1.5.128.
- CWE(s)
AI Security AnalysisAI
- AI Category
- AI Agent Protocols and Integrations
- Risk Domain
- LLM/Generative AI Risks
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: ai, prompt injection
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.