CVE-2026-107287 (GCVE-0-2026-107287)
Vulnerability from cvelistv5 – Published: 2026-10-08 15:15 – Updated: 2026-10-08 15:15
VLAI
EPSS
VEX
Title
Pydantic AI: Excessive resource use when local web fetching converts nested HTML
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.
Severity
6.5 (Medium)
Assigner
References
7 references
| URL | Tags |
|---|---|
| https://github.com/pydantic/pydantic-ai/security/… | x_refsource_CONFIRM |
| https://github.com/pydantic/pydantic-ai/pull/8984 | x_refsource_MISC |
| https://github.com/pydantic/pydantic-ai/pull/8985 | x_refsource_MISC |
| https://github.com/pydantic/pydantic-ai/commit/2b… | x_refsource_MISC |
| https://github.com/pydantic/pydantic-ai/commit/2f… | x_refsource_MISC |
| https://github.com/pydantic/pydantic-ai/releases/… | x_refsource_MISC |
| https://github.com/pydantic/pydantic-ai/releases/… | x_refsource_MISC |
Impacted products
2 products
| Vendor | Product | Version | |
|---|---|---|---|
| pydantic | pydantic-ai |
Affected:
>= 1.77.0, < 1.107.7
Affected: >= 2.0.0b1, < 2.52.0 |
|
| pydantic | pydantic-ai-slim |
Affected:
>= 1.77.0, < 1.107.7
Affected: >= 2.0.0b1, < 2.52.0 |
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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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