Raw vector
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:XCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2025-32392 is a high-severity Uncontrolled Resource Consumption (CWE-400) vulnerability. Its CVSS base score is 8.7 (High).
Operationally, ranked at the 35th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as LLM Application Platforms.
The strongest mitigations our analysis identified map to SC-5 (Denial-of-service Protection) and SC-6 (Resource Availability) — see the control section below for these in your framework.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-210279
Vulnerability Data
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…
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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.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- N/A
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: artificial intelligence
Related Threats
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
SC-5 directly limits the effects of resource-exhaustion events that constitute uncontrolled consumption.
SC-6 enforces explicit allocation limits on resources, structurally preventing the weakness from occurring.
Process isolation confines resource consumption to separate domains, reducing blast radius without stopping the root flaw.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Continuous monitoring of computing resources can detect resource exhaustion but does not itself enforce allocation limits.
Resilience mechanisms such as avoiding single points of failure indirectly reduce impact of resource exhaustion.
Explicitly requires monitoring and maintaining resource capacity, directly addressing uncontrolled consumption to preserve availability.
Hardened configuration baselines can include resource quotas and limits that constrain consumption.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Pre-agreed severity-based prioritization and resource allocation during incident triage reduce the likelihood that an attacker-induced resource exhaustion will overwhelm the organization before corrective action is taken.
Early notification of anomalous resource consumption or system malfunctions enables throttling or isolation before availability is lost.
Specifying log storage limits and rotation procedures reduces the risk that unbounded log growth will exhaust disk or memory resources and cause denial of service.
Resource-utilization monitoring and alerting on bottlenecks or overloads limits the impact of denial-of-service or resource-exhaustion attacks.
A.8.20 implements network-level protections (firewalls, segmentation, protocol hardening, monitoring, authentication of systems/devices) that can limit abuse of a resource-consuming weakness but do not stop the weakness from being exploitable; the flaw remains present in the product and can still be triggered from an authorized or internal context.
Monitoring network service usage and applying controls to restrict access when necessary limits an attacker’s ability to consume excessive resources through unrestricted or unauthenticated service calls.