FKIE_CVE-2026-107287
Vulnerability from fkie_nvd - Published: 2026-10-08 16:17 - Updated: 2026-10-08 21:05
Severity
Summary
Pydantic AI is a Python agent framework for building applications and workflows with Generative AI. From 1.77.0 until 1.107.7 and 2.52.0, the local web_fetch_tool and the WebFetch local fallback can consume excessive CPU and memory during HTML-to-Markdown conversion of attacker-controlled HTML containing deeply nested block elements. Conversion repeatedly reprocesses accumulated text and can greatly expand intermediate output before the returned-content limit is applied, allowing a model-directed fetch to delay other work in the process. Provider-native web fetching is not affected. This issue is fixed in versions 1.107.7 and 2.52.0.
References
Impacted products
| Vendor | Product | Version |
|---|
{
"affected": [
{
"affectedData": [
{
"product": "pydantic-ai",
"vendor": "pydantic",
"versions": [
{
"status": "affected",
"version": "\u003e= 1.77.0, \u003c 1.107.7"
},
{
"status": "affected",
"version": "\u003e= 2.0.0b1, \u003c 2.52.0"
}
]
},
{
"product": "pydantic-ai-slim",
"vendor": "pydantic",
"versions": [
{
"status": "affected",
"version": "\u003e= 1.77.0, \u003c 1.107.7"
},
{
"status": "affected",
"version": "\u003e= 2.0.0b1, \u003c 2.52.0"
}
]
}
],
"source": "security-advisories@github.com"
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "Pydantic AI is a Python agent framework for building applications and workflows with Generative AI. From 1.77.0 until 1.107.7 and 2.52.0, the local web_fetch_tool and the WebFetch local fallback can consume excessive CPU and memory during HTML-to-Markdown conversion of attacker-controlled HTML containing deeply nested block elements. Conversion repeatedly reprocesses accumulated text and can greatly expand intermediate output before the returned-content limit is applied, allowing a model-directed fetch to delay other work in the process. Provider-native web fetching is not affected. This issue is fixed in versions 1.107.7 and 2.52.0."
}
],
"id": "CVE-2026-107287",
"lastModified": "2026-10-08T21:05:00.260",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 6.5,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "REQUIRED",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:H",
"version": "3.1"
},
"exploitabilityScore": 2.8,
"impactScore": 3.6,
"source": "security-advisories@github.com",
"type": "Secondary"
}
]
},
"published": "2026-10-08T16:17:03.973",
"references": [
{
"source": "security-advisories@github.com",
"url": "https://github.com/pydantic/pydantic-ai/commit/2b247add4950bef61d352e7ca8aefbd20539180c"
},
{
"source": "security-advisories@github.com",
"url": "https://github.com/pydantic/pydantic-ai/commit/2fd38792693da00a3ca5412aeffb436787af3545"
},
{
"source": "security-advisories@github.com",
"url": "https://github.com/pydantic/pydantic-ai/pull/8984"
},
{
"source": "security-advisories@github.com",
"url": "https://github.com/pydantic/pydantic-ai/pull/8985"
},
{
"source": "security-advisories@github.com",
"url": "https://github.com/pydantic/pydantic-ai/releases/tag/v1.107.7"
},
{
"source": "security-advisories@github.com",
"url": "https://github.com/pydantic/pydantic-ai/releases/tag/v2.52.0"
},
{
"source": "security-advisories@github.com",
"url": "https://github.com/pydantic/pydantic-ai/security/advisories/GHSA-v36g-jcw9-x7cw"
}
],
"sourceIdentifier": "security-advisories@github.com",
"vulnStatus": "Undergoing Analysis",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-400"
},
{
"lang": "en",
"value": "CWE-407"
}
],
"source": "security-advisories@github.com",
"type": "Primary"
}
]
}
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
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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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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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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