Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2026-39890 is a critical-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Praison Praisonai. Its CVSS base score is 9.8 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 45th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) 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-2026-39890 is a critical deserialization vulnerability (CWE-502) in PraisonAI, an open-source multi-agent teams system. Prior to version 4.5.115, the AgentService.loadAgentFromFile method parses YAML files using the js-yaml library without disabling dangerous tags like !!js/function and !!js/undefined. This flaw enables attackers to execute arbitrary JavaScript code during parsing of untrusted YAML input. The vulnerability carries a CVSS v3.1 base score of 9.8 (AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H), indicating high severity due to its network accessibility and lack of prerequisites.
Any unauthenticated remote attacker can exploit this vulnerability by crafting a malicious YAML file containing JavaScript payloads and uploading it as an agent definition via the PraisonAI API endpoint. Successful exploitation leads to remote code execution (RCE) on the server hosting the application, potentially allowing full compromise of the environment, data exfiltration, or further lateral movement.
The vulnerability is addressed in PraisonAI version 4.5.115, where the parsing logic was updated to mitigate the dangerous YAML tags. Security practitioners should upgrade to this version or later. Official details are available in the GitHub Security Advisory at https://github.com/MervinPraison/PraisonAI/security/advisories/GHSA-32vr-5gcf-3pw2.
As PraisonAI supports multi-agent AI workflows, this RCE flaw could impact AI/ML deployments relying on dynamic agent loading from files, though no public evidence of real-world exploitation has been reported as of the CVE publication on 2026-04-08.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-20638
Vulnerability Data
PraisonAI is a multi-agent teams system. Prior to 4.5.115, the AgentService.loadAgentFromFile method uses the js-yaml library to parse YAML files without disabling dangerous tags (such as !!js/function and !!js/undefined). This allows an attacker to craft a malicious YAML file that,…
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when parsed, executes arbitrary JavaScript code. An attacker can exploit this vulnerability by uploading a malicious agent definition file via the API endpoint, leading to remote code execution (RCE) on the server. This vulnerability is fixed in 4.5.115.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can uncover deserialization flaws before deployment.
Input validation directly stops deserialization of untrusted data by ensuring inputs are valid before processing.
Engineering principles such as safe deserialization and input sanitization structurally prevent the weakness from being introduced.
Integrity verification tools can detect malformed or tampered serialized data after the fact.
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.
PR.PS-02 addresses only post-deployment updates/patching and cannot prevent introduction of unsafe deserialization code, yet it can remediate some instances when the flaw exists in outdated libraries or components.
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.
Security testing includes validation of deserialization routines and the use of untrusted data, reducing the likelihood that unsafe object reconstruction will be deployed.
Requiring vetted libraries, regular updates and SAST before release reduces the likelihood that deserialization logic will accept and act on attacker-controlled serialized objects.
Regular scanning of third-party libraries and timely patching reduce the likelihood that unsafe deserialization vulnerabilities remain active.
Mandatory malware scanning of data received over networks or storage media intercepts malicious serialized payloads before they are deserialized by the target application.