CVE-2026-107291 (GCVE-0-2026-107291)

Vulnerability from cvelistv5 – Published: 2026-10-08 16:22 – Updated: 2026-10-08 17:25
VLAI
Title
Pydantic AI OpenTelemetry instrumentation: exception events on tool and agent run spans include content when `include_content=False`
Summary
Pydantic AI is a Python agent framework for building applications and workflows with Generative AI. From 0.3.4 until 1.107.6 and 2.44.0, OpenTelemetry instrumentation configured with InstrumentationSettings(include_content=False) can still export sensitive agent content through exception.message and exception.stacktrace events, error status descriptions, and model_request_parameters containing instructions or the prompted_output_template. The exposed data is available to readers of the configured telemetry backend and can include tool feedback, provider error bodies, runtime instructions, and structured-output templates even though message attributes are redacted. This issue does not grant new access to agent data, and deployments that do not use include_content=False are not affected by the setting bypass. This issue is fixed in versions 1.107.6 and 2.44.0.
SSVC
Exploitation: none Automatable: no Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-10-08 17:25 UTC
CWE
  • CWE-212 - Improper Removal of Sensitive Information Before Storage or Transfer
  • CWE-532 - Insertion of Sensitive Information into Log File
Assigner
GitHub_M CNA under the mitre root
CNA scorecard C 72/100 over 7595 records in the last 180 days details
Impacted products
Vendor Product Version CPE status
pydantic pydantic-ai Affected: >= 0.3.4, < 1.107.6
Affected: >= 2.0.0b1, < 2.44.0
guessed Create a notification for this product.
pydantic pydantic-ai-slim Affected: >= 0.3.4, < 1.107.6
Affected: >= 2.0.0b1, < 2.44.0
guessed Create a notification for this product.
Show details on NVD website

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        "dateReserved": "2026-10-07T15:53:23.586Z",
        "dateUpdated": "2026-10-08T17:25:18.280Z",
        "state": "PUBLISHED"
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      "dataVersion": "5.2"
    }
  }
}



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Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.

Sightings

Author Source Type Date Other

Nomenclature

  • Seen: The vulnerability was mentioned, discussed, or observed by the user.
  • Confirmed: The vulnerability has been validated from an analyst's perspective.
  • Published Proof of Concept: A public proof of concept is available for this vulnerability.
  • Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
  • Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
  • Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
  • Not confirmed: The user expressed doubt about the validity of the vulnerability.
  • Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.

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Related by attack behaviour

Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.


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