MLflow CrewAI autologging records LLM provider API keys in logged trace spans
CVSS score
6.5
EPSS probability
—
Risk score
0.7
Published
3 h ago
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Vulnerability description
DayBreach AI Explanation
Verified against sourcesSources read
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Source agreement
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Evidence confidence
%55
Short explanation
The mlflow.crewai.autolog() function in MLflow from version 2.19.0 to 3.17.0 records CrewAI Agent objects without sanitizing embedded credentials. This flaw causes LLM provider API keys to be sent in plaintext to the tracing backend or OTLP destinations.
This vulnerability creates an information disclosure risk by allowing unauthorized trace readers to obtain sensitive provider API keys.
What should be done?
- Follow vendor advisories and guidance
- Update affected instances
- Review autologging configurations
Chain of evidence and sources
mlflow.crewai.autolog() fonksiyonu 2.19.0 ile 3.17.0 sürümleri arasında kimlik bilgilerini temizlemez.
Ajan nesnelerinin işlenmesi, sağlayıcı API anahtarlarının izleme arka ucuna açık metin olarak gitmesine yol açar.
Show the technical text from the sources
MLflow is an open source AI engineering platform for agents, large language models, and machine learning models. From 2.19.0 until 3.17.0, mlflow.crewai.autolog() records CrewAI Agent objects without consistently sanitizing nested credential-bearing values. mlflow/crewai/autolog.py::_get_agent_attributes() converts the Agent llm field to a string that includes the provider API key, while mlflow/crewai/autolog.py::_construct_full_inputs() expands the Agent object into mlflow.spanInputs. Applications can therefore send plaintext provider credentials to the MLflow trace backend or a configured OTLP destination, where trace readers can obtain them. This issue is fixed in version 3.17.0.
CVSS vector: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:N
CWE: CWE-200, CWE-312, CWE-532
This explanation was compiled automatically from the source records. Scores, active exploitation, patch and exploit status come straight from the sources.
IMPACT: Affected Products and Software
RED HAT CSAF/VEX: Product Impact Check
SCORING: CVSS from Multiple Sources
CVSS Scores
The Common Vulnerability Scoring System is a standardised framework for assessing the severity of vulnerabilities in software and systems. We collect and display CVSS scores from several sources for each CVE.
| SCORE | VERSION | SEVERITY | VECTOR | SOURCE |
|---|---|---|---|---|
| 6.5 | CVSS V31 | MEDIUM | CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:N | NVD |
| 6.5 | CVSS 3.x | — | Vector not published | GitHub Advisory |
| 6.5 | CVSS V3_1 | MEDIUM | CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:N | CVE CNA |
| 0.0 | CVSS 4.0 | — | Vector not published | GitHub Advisory |
Patch and Remediation Guide
Solution & Remediation Advisory
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Public exploit and active exploitation status
Verified exploit intelligence
No verified public PoC, exploit repository or Metasploit module has been found for this CVE yet.
Weakness Classification and Attack Patterns (CWE & CAPEC)
CWE - Common Weakness Enumeration
While CVE identifies specific instances of vulnerabilities, CWE categorises the common flaws or weaknesses that can lead to vulnerabilities. CVE-2026-116691 is associated with the following CWEs:
Common Attack Pattern Enumeration and Classification (CAPEC)
Common Attack Pattern Enumeration and Classification (CAPEC) stores attack patterns, which are descriptions of the common attributes and approaches employed by adversaries to exploit the CVE-2026-116691 weaknesses.
Attack Vector and Accessibility
Official Sources and Advisories
Similar Vulnerabilities
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