CVE-2024-5616 (GCVE-0-2024-5616)
Vulnerability from cvelistv5 – Published: 2024-07-06 08:38 – Updated: 2024-08-01 21:18
VLAI
EPSS
VEX
Title
CSRF Vulnerability in mudler/LocalAI
Summary
A Cross-Site Request Forgery (CSRF) vulnerability exists in mudler/LocalAI versions up to and including 2.15.0, which allows attackers to trick victims into deleting installed models. By crafting a malicious HTML page, an attacker can cause the deletion of a model, such as 'gpt-4-vision-preview', without the victim's consent. The vulnerability is due to insufficient CSRF protection mechanisms on the model deletion functionality.
Severity
4.3 (Medium)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-07-22 17:54 UTC
CWE
- CWE-352 - Cross-Site Request Forgery (CSRF)
Assigner
References
Impacted products
2 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| mudler | mudler/localai |
Affected:
unspecified , < 2.17
(custom)
|
guessed | |
| mudler | localai |
Affected:
0 , < 2.17
(custom)
cpe:2.3:a:mudler:localai:*:*:*:*:*:*:*:* |
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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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