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CVE-2026-72904 – Arbitrary File Read / SSRF – Firecrawl before 2.11.32

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Summary

CVE-2026-72904 is a critical arbitrary file read and server-side request forgery (SSRF) vulnerability in Firecrawl, the tool that converts websites into LLM-ready markdown or structured data. Firecrawl versions prior to 2.11.32 mishandle user-supplied JSON schemas during extraction, allowing an authenticated attacker to disclose files and secrets from the extract worker. The issue carries a CVSS 4.0 base score of 9.3 (Critical).

Technical details

  • Root cause: The extraction pipeline dereferences user-supplied JSON schemas in apps/api/src/lib/extract/helpers/dereference-schema.ts using @apidevtools/json-schema-ref-parser with default resolver settings, which permits resolution of external and local file references.
  • Trigger conditions: An attacker submits a malicious schema containing a $ref inside default, const, or enum fields — fields not traversed by AJV validation. Triggering a dereference error causes file contents to be included in persisted error messages returned through the extraction API.
  • Attack vector: Network. Requires authenticated API access (including free-tier accounts); no user interaction.
  • Impact: Disclosure of arbitrary files readable by the extract worker process, exfiltration of environment variables and application secrets, and SSRF against internal or external HTTP(S) endpoints.

Affected software

  • Firecrawl versions prior to 2.11.32 (including self-hosted/Docker deployments).

Severity

  • CVSS 4.0 base score: 9.3 (Critical)
  • Vector: CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N

Mitigation and recommended actions

  • Immediate: Upgrade to Firecrawl 2.11.32 or later. The patch replaces the vulnerable dependency with an internal-only $ref resolver (commit 053630f). Self-hosted users should re-pull the :latest image or upgrade to 2.11.32.
  • If no patch can be applied: Restrict network access to the Firecrawl API to trusted clients, avoid exposing the instance directly to the internet, and rotate any secrets or environment variables that may have been accessible to the extract worker.

How IONIX identifies potentially affected assets

IONIX matches the following signals against data already collected when it crawled the asset; identifying the technology sends no request beyond that crawl.

  • Raw response body: "message": "Firecrawl API", SCRAPERS-JS: Hello, world! Fly.io
  • www-authenticate response header: Bearer resource_metadata="https://www.firecrawl.dev/.well-known/oauth-protected-resource"

References

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How IONIX’s External Exposure Management Platform Detects and Validates
Zero-Days to Shrink MTTR

1

Map your entire attack surface (continously)

IONIX uses multi-factor discovery methods, including DNS analysis, certificate mapping, metadata inspection, and more, to automatically map every internet-facing asset across your environment. This includes cloud instances, third-party platforms, shadow IT, and even forgotten infrastructure that traditional tools miss.

2

Monitor for new CVEs

Dozens of threat intel feeds using agentic technology are continuously analyzed to detect the appearance of proof-of-concept code, exploit kits, and indicators of active targeting. IONIX goes further by applying AI to proactively evaluate whether emerging vulnerabilities are likely to be exploited, even before PoCs go public.

3

Identify Potential External Exposures

Not all CVEs matter. IONIX filters vulnerabilities by asking attacker-centric questions: Can it be reached from the internet? Does it require authentication? Is it being exploited in the wild? This dramatically reduces noise and focuses teams on threats that can actually be weaponized.

4

Create Safe, Scalable Exploit Validations

IONIX transforms real-world PoCs into safe, non-intrusive test payloads that can be run in production environments without disruption. These simulations are precisely targeted to the systems that are vulnerable, ensuring rapid validation without unnecessary load.

5

Execute Exploit Validations

By combining context about software stack, versioning, exposure status, and reachability, IONIX ensures that only the right payloads are executed against the right assets, maximizing efficiency and minimizing risk.

6

Drive Fast and Actionable Remediation

Results are routed through integrations with ticketing, SOAR, and SIEM tools. Issues are written in plain language, bundled into remediation clusters, and prioritized based on asset criticality, exploitability, and blast radius. This shortens mean time to remediation (MTTR) and empowers teams to act with confidence.

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  • Mapping of all Assets with this Technology
  • Identification of potentially exposed assets to this CVE
  • Confirmation of verified exploitable assets
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