Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:N/A:NSummary
CVE-2026-34936 is a high-severity SSRF (CWE-918) vulnerability in Praison Praisonai. 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 26th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
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-2026-34936 is a server-side request forgery (SSRF) vulnerability (CWE-918) in PraisonAI, an open-source multi-agent teams system. In versions prior to 4.5.90, the passthrough() and apassthrough() functions accept a caller-controlled api_base parameter, which is concatenated with an endpoint and passed directly to httpx.Client.request() if the litellm primary path raises an AttributeError. The implementation lacks URL scheme validation, private IP filtering, or domain allowlisting, enabling requests to arbitrary hosts reachable from the server.
The vulnerability has a CVSS v3.1 base score of 7.7 (AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:N/A:N), indicating network accessibility with low attack complexity and requiring only low privileges. An authenticated attacker with low privileges can exploit this by supplying a malicious api_base, tricking the server into making unauthorized outbound requests. This allows access to internal services or metadata endpoints behind the server's network boundaries, resulting in high confidentiality impact through data exfiltration, while integrity and availability remain unaffected.
The issue was addressed in PraisonAI version 4.5.90. Security practitioners should upgrade to this version or later. Additional details, including patch information, are available in the GitHub Security Advisory at https://github.com/MervinPraison/PraisonAI/security/advisories/GHSA-x6m9-gxvr-7jpv.
PraisonAI's role as a multi-agent AI system underscores the relevance of this SSRF flaw in AI/ML workflows, where untrusted inputs could propagate through agent interactions to compromise internal resources. No public evidence of real-world exploitation has been reported as of the CVE publication on 2026-04-03.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-18915
Vulnerability Data
PraisonAI is a multi-agent teams system. Prior to version 4.5.90, passthrough() and apassthrough() in praisonai accept a caller-controlled api_base parameter that is concatenated with endpoint and passed directly to httpx.Client.request() when the litellm primary path raises AttributeError. No URL scheme…
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validation, private IP filtering, or domain allowlist is applied, allowing requests to any host reachable from the server. This issue has been patched in version 4.5.90.
- CWE(s)
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.