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Uncontrolled Resource ConsumptionWeakness · CWE-400

CVE-2025-32392

HIGH · 8.7 CVSS v4.0 Published 2026-06-18
Mitigation only
No fix yet — a mitigation exists. There is no fixed release. A documented workaround reduces exposure in the meantime.
See remediation →
96/100
Remediation priority · Urgent
Remotely reachable No privileges Zero-click

Official description Straight from the sourceThe vendor's or NVD's own wording, published unedited. Authoritative, but often terse — it says what broke, rarely what to do.

NVD · unedited
AutoGPT is a workflow automation platform for creating, deploying, and managing continuous artificial intelligence agents. Prior to 0.6.63, AutoGPT's LoopVideoBLock allows users to input a video file and process the video, such as looping it 5 times or extending the time, and finally writing it to disk. However, there is no limit on the resources that can be allocated during execution. For example, the number of loops is user-controllable and unlimited. When a malicious attacker loops too many times, the generated video is too large, and after writing it to disk, the disk space is exhausted, eventually causing DoS. Version 0.6.63 patches the issue.

Technical summary Written by usOur analysis, written from the advisory, the CVSS vector and the affected-version data. It adds context the advisory leaves out, and never invents facts that are not in the source.

dbcve analysis · high confidence

The LoopVideoBlock in AutoGPT before version 0.6.63 lacks resource limits on user-controllable loop parameters, allowing attackers to specify unlimited video loop iterations that generate excessively large files, exhausting disk space and causing denial of service.

MitigationUpgrade to version 0.6.63 or later which implements proper resource limits. Additionally, implement input validation to restrict maximum loop counts and add disk space availability checks before video processing operations.

Verify against the referenced sources before acting — the references below are authoritative for this CVE, this summary is not.

CVSS breakdown How the score is builtThe industry scoring standard. It rates how the flaw is reached, what it takes to exploit, and what an attacker gains — the score is derived from those, not the other way round.

From the vector
Attack vector
Network
Complexity
Low
Privileges
None
Authentication
X
User interaction
None
Scope
X

CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X

Am I affected? How to checkSteps we derive from the advisory and the affected-version data, so you can decide whether this CVE reaches your setup. They are a guide, not a scan — your own configuration is the authority.

dbcve checks

Work through these to decide whether this CVE applies to you.

  1. Identify AutoGPT installation
    Locate the AutoGPT installation directory and find the version file or package metadata (commonly in pyproject.toml, setup.py, or a VERSION file in the project root)
    Affected if AutoGPT is installed and the version is earlier than 0.6.63
  2. Verify the LoopVideoBlock module exists
    Search for LoopVideoBlock or loop_video_block in the codebase, typically under src/autogpt/plugins/video or similar plugin directories
    Affected if The LoopVideoBlock module is present in the installation
  3. Check for loop count resource limits
    Inspect the LoopVideoBlock source code for any max_loop, limit, or max_iterations parameters that restrict the loop count value
    Affected if No resource limit parameters exist or they can be bypassed by user input
  4. Inspect the loop parameter configuration
    Review the LoopVideoBlock configuration files or API endpoints to determine if loop_count is a user-controllable parameter without validation
    Affected if loop_count can be set directly by user input without validation or upper bounds
  5. Check available disk space before operation
    Review the video processing code to see if disk space checks occur before generating looped video files
    Affected if No disk space validation exists prior to video loop generation

The environment is affected if AutoGPT version is earlier than 0.6.63 and the LoopVideoBlock feature with user-controllable loop parameters is in use without resource limits.

Generated from the published advisory. Verify against your own configuration.

Check your environment

Paste your version and any relevant configuration and it will be compared against the affected criteria above. Do not include secrets or credentials.

AI-assisted, checked against the advisory. Informational, not a guarantee.

Remediation Closing itWhat it takes to close this. Where a vendor fix exists we point at it; where none exists we say so plainly, and can build one. Effort estimates are scoped from the advisory, not from your codebase.

dbcve · scoped
Mitigation available No clean upgrade yet — mitigate in the meantime
Mitigation

Upgrade to version 0.6.63 or later which implements proper resource limits. Additionally, implement input validation to restrict maximum loop counts and add disk space availability checks before video processing operations.

Recommended fix Moderate confidence

0.6.63

  1. 1. Identify the AutoGPT installation in your environment
  2. 2. Check the current installed version (likely via package.json, requirements.txt, or the application itself)
  3. 3. Upgrade AutoGPT to version 0.6.63 or later
  4. 4. Verify the upgrade was successful by checking the new version number
  5. 5. Test that the LoopVideoBlock functionality works correctly with the new resource limits

Generated from the published advisory — verify against the referenced sources before acting.

Have this fixed Scoped from the published advisory
  • Consultation2.0 h
  • Implementation4.0 h
  • Testing3.0 h
  • Review / QA1.0 h
10.0 hours of engineering $1,750
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References Go to the primary sourcePrimary sources — vendor advisories, patches and trackers. Where our summary and a reference disagree, the reference wins.

Primary sources

Practitioner notes

Contributed

Peer-ranked notes from engineers who’ve handled CVE-2025-32392 in production — separate from our analysis above.

No notes yet

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What this is

A place for practitioners to share what actually worked: a mitigation you’ve tested, a configuration change, a version- or environment-specific caveat, or a link to a verified patch. The most useful notes rise to the top as peers upvote them, so the signal stays high.

What belongs here
  • Verified mitigations, workarounds, and config changes
  • Version or environment caveats, and links to real fixes
  • No weaponised exploit code, or anything meant to cause harm
  • No spam, self-promotion, credentials, or personal data