CVE-2025-10965 (GCVE-0-2025-10965)
Vulnerability from cvelistv5 – Published: 2025-09-25 20:02 – Updated: 2025-09-26 17:56
VLAI
EPSS
VEX
Title
LazyAGI LazyLLM server.py lazyllm_call deserialization
Summary
A security vulnerability has been detected in LazyAGI LazyLLM up to 0.6.1. Affected by this issue is the function lazyllm_call of the file lazyllm/components/deploy/relay/server.py. Such manipulation leads to deserialization. The attack can be launched remotely. The exploit has been disclosed publicly and may be used.
Severity
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-09-26 17:38 UTC
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://vuldb.com/?id.325833 | vdb-entrytechnical-description |
| https://vuldb.com/?ctiid.325833 | signaturepermissions-required |
| https://vuldb.com/?submit.652936 | third-party-advisory |
| https://github.com/LazyAGI/LazyLLM/issues/764 | exploitissue-tracking |
Impacted products
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
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.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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