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AI security incident: CVE-2026-57573 (NVD)

Source disclosure: July 6, 2026 | Published on Radar: October 13, 2026 | Source: NVD | Classification confidence: 45%

Source disclosure is when the advisory appeared in this source (or was added to a catalog), not a confirmed date of exploitation. Radar publication is when this page became available.

This incident is part of the public archive and includes explicit AI-related signals from the cited source material. Review methodology.

Crawl4AI is an open-source LLM-friendly web crawler and scraper. Prior to 0.9.0, the Docker API server applied its SSRF destination check on the non-streaming /crawl path but not on the streaming path.

Why This Is AI-Related

This page is treated as AI-specific because the source material references llm, which places the issue inside an AI workflow, model, assistant, or supporting dependency rather than a generic software bulletin.

  • llm

Affected Workflow

Review connectors, retrieval plugins, webhook targets, file access paths, and outbound network policies around AI services.

Likely Attack Path

The flaw creates an unauthorized path to fetch, read, or exfiltrate sensitive data from connected systems or local files.

Impact

The flaw can expose internal data, local files, or connected systems through AI workflow connectors and supporting services. Severity HIGH. Classification confidence 45%. Source channel NVD.

Detection And Triage Signals

  • Unexpected outbound requests from AI application components
  • Access to internal metadata endpoints, local files, or restricted datasets
  • Downloads or responses that contain internal documents, secrets, or embeddings

Recommended Response

  • Confirm whether the affected component can reach internal metadata, local files, or connected data stores.
  • Restrict outbound requests and sensitive data access paths until a patch or mitigation is in place.
  • Inspect logs for unusual downloads, webhook calls, retrieval requests, or responses containing internal content.

Compliance And Business Impact

Data exposure creates direct confidentiality risk and can trigger incident notification, contractual, and regulatory obligations.

Sources

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