BREW-OTERM-CVE-2026-107287 (GHSA-V36G-JCW9-X7CW)

Vulnerability from osv_homebrew – Published: 2026-10-08 18:40 – Updated: 2026-10-08 18:40 – Source website
VLAI
Summary
Pydantic AI: Excessive resource use when local web fetching converts nested HTML
Details

Summary

Applications using Pydantic AI's local web-fetch tool can experience excessive CPU and memory use when it converts attacker-controlled HTML. An agent must fetch the affected page; provider-native web fetching is not affected.

Details

Nested block elements cause HTML-to-Markdown conversion to reprocess accumulated text at each level and can greatly expand the intermediate output. The response-body limit bounds downloaded bytes, while the returned-content limit is applied only after conversion. On current releases, conversion runs in a worker thread but can still consume substantial resources and delay other work in the process. Older releases performed conversion on the event loop.

Mitigation

Upgrade to a patched release of pydantic-ai or pydantic-ai-slim. If you cannot upgrade yet, avoid using local web fetching for attacker-controlled HTML.


{
  "affected": [
    {
      "ecosystem_specific": {
        "fix": null,
        "range_state": "affected",
        "resource": "pydantic-ai-slim",
        "resource_purl": "pkg:pypi/pydantic-ai-slim@2.51.0",
        "upstream_fixed_in": "2.52.0"
      },
      "package": {
        "ecosystem": "Homebrew",
        "name": "oterm",
        "purl": "pkg:brew/oterm"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0.15.0"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "database_specific": {
    "confidence": "high",
    "source": "matched",
    "strategy": "registry",
    "upstream_evidence": [
      {
        "ecosystem": "PyPI",
        "key": "pkg:pypi/pydantic-ai-slim@2.51.0",
        "name": "pydantic-ai-slim",
        "resource": "pydantic-ai-slim",
        "strategy": "registry",
        "subject_version": "2.51.0"
      }
    ]
  },
  "details": "### Summary\n\nApplications using Pydantic AI\u0027s local web-fetch tool can experience excessive CPU and memory use when it converts attacker-controlled HTML. An agent must fetch the affected page; provider-native web fetching is not affected.\n\n### Details\n\nNested block elements cause HTML-to-Markdown conversion to reprocess accumulated text at each level and can greatly expand the intermediate output. The response-body limit bounds downloaded bytes, while the returned-content limit is applied only after conversion. On current releases, conversion runs in a worker thread but can still consume substantial resources and delay other work in the process. Older releases performed conversion on the event loop.\n\n### Mitigation\n\nUpgrade to a patched release of `pydantic-ai` or `pydantic-ai-slim`. If you cannot upgrade yet, avoid using local web fetching for attacker-controlled HTML.",
  "id": "BREW-oterm-CVE-2026-107287",
  "modified": "2026-10-08T18:40:35Z",
  "published": "2026-10-08T18:40:35Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/pydantic/pydantic-ai/security/advisories/GHSA-v36g-jcw9-x7cw"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pydantic/pydantic-ai/pull/8984"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pydantic/pydantic-ai/pull/8985"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pydantic/pydantic-ai/commit/2b247add4950bef61d352e7ca8aefbd20539180c"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pydantic/pydantic-ai/commit/2fd38792693da00a3ca5412aeffb436787af3545"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/pydantic/pydantic-ai"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pydantic/pydantic-ai/releases/tag/v1.107.7"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pydantic/pydantic-ai/releases/tag/v2.52.0"
    }
  ],
  "schema_version": "1.7.3",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:H",
      "type": "CVSS_V3"
    }
  ],
  "summary": "Pydantic AI: Excessive resource use when local web fetching converts nested HTML",
  "upstream": [
    "GHSA-v36g-jcw9-x7cw",
    "CVE-2026-107287"
  ]
}



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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.

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