Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:HSummary
CVE-2026-22038 is a high-severity Insertion of Sensitive Information into Log File (CWE-532) vulnerability in Agpt Autogpt Platform. Its CVSS base score is 8.1 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Unsecured Credentials (T1552); ranked at the 36th 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 Privacy and Disclosure risk domain.
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-2026-22038 is a vulnerability in the AutoGPT platform, which enables users to create, deploy, and manage continuous AI agents for automating complex workflows. In versions prior to autogpt-platform-beta-v0.6.46, the Stagehand integration's block implementations—specifically StagehandObserveBlock, StagehandActBlock, and StagehandExtractBlock—log API keys and authentication secrets in plaintext. This occurs through explicit calls to api_key.get_secret_value() followed by logger.info() statements, violating CWE-532 (Insertion of Sensitive Information into Log File). The issue carries a CVSS v3.1 base score of 8.1 (AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:H), highlighting high confidentiality and availability impacts.
An attacker with low privileges, such as a legitimate user or service account with network access to the AutoGPT platform, can exploit this vulnerability with low complexity and no user interaction required. By accessing the logs generated by the affected Stagehand blocks, the attacker can extract plaintext API keys and authentication secrets, enabling unauthorized access to integrated services, potential lateral movement, or further compromise depending on the secrets' scope. The high confidentiality impact stems directly from secret exposure, while the availability impact likely arises from disruptions possible after credential misuse.
The vulnerability has been patched in autogpt-platform-beta-v0.6.46, as detailed in the GitHub security advisory (GHSA-rc89-6g7g-v5v7) and the fixing commit (1eabc604842fa876c09d69af43d2d1e8fb9b8eb9). Security practitioners should upgrade to the patched version and review existing logs for exposed secrets, implementing log monitoring, rotation, and redaction controls to mitigate risks in AI agent platforms like AutoGPT.
This issue is particularly relevant in AI/ML contexts, as AutoGPT handles autonomous agents that often integrate sensitive third-party APIs for tasks like observation, action, and extraction in workflows. No public evidence of real-world exploitation is available as of the CVE publication on 2026-02-04.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-5543
Vulnerability Data
AutoGPT is a platform that allows users to create, deploy, and manage continuous artificial intelligence agents that automate complex workflows. Prior to autogpt-platform-beta-v0.6.46, the AutoGPT platform's Stagehand integration blocks log API keys and authentication secrets in plaintext using logger.info() statements.…
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This occurs in three separate block implementations (StagehandObserveBlock, StagehandActBlock, and StagehandExtractBlock) where the code explicitly calls api_key.get_secret_value() and logs the result. This issue has been patched in autogpt-platform-beta-v0.6.46.
- CWE(s)
AI Security AnalysisAI
- AI Category
- AI Agent Protocols and Integrations
- Risk Domain
- Privacy and Disclosure
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: artificial intelligence, autogpt
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Likely Mitigating Controls AI
Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.
Procedures mandate excluding sensitive data from logs to prevent unauthorized exposure via audit records.
Identifies insertion of sensitive data into logs, allowing detection of unauthorized disclosure.
Cross-organizational coordination enables agreement on what data to include in audit logs, directly reducing insertion of sensitive information.
Identifying logging as a data action allows prevention of sensitive information being inserted into log files.
The process of identifying and eradicating spilled information applies directly to sensitive data inserted into log files.
Specific processing rules for sensitive PII categories commonly include restrictions on logging, making insertion of such data into log files less likely.
PIAs detect planned or existing logging of PII and require removal or protection, preventing insertion of sensitive information into logs.
Limits insertion of sensitive operational details into logs by treating such data as key information requiring protection.
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 SDLC practices directly prohibit writing sensitive data to logs; eliminating the weakness satisfies only one narrow slice of the control.
Log generation configuration can and should exclude sensitive data, but the control statement focuses on availability rather than content filtering.
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.
Requiring de-identification and privacy controls before logs leave the organization reduces the chance that sensitive data inadvertently captured in logs becomes exposed to external parties.
By defining what records must be kept, where, and for how long, the control discourages the inadvertent inclusion of sensitive information in logs or other externally accessible files that fall outside the formal record system.
Mandating deletion of temporary files and logs that may contain sensitive information prevents those artifacts from remaining accessible after the data is no longer needed.
When log entries are produced from masked data sets, the control prevents the inadvertent insertion of sensitive values into externally accessible log files.
DLP inspection of logs and file transfers can detect and block the inadvertent placement of sensitive tokens or credentials into externally accessible log files before they are written or transmitted.
Requiring restrictions on free-text fields and proper error-message handling stops developers from embedding or leaking sensitive data into logs or diagnostic output.