CVE-2025-27152 (GCVE-0-2025-27152)
Vulnerability from cvelistv5 – Published: 2025-03-07 15:13 – Updated: 2025-03-07 19:32
VLAI
EPSS
VEX
Title
Possible SSRF and Credential Leakage via Absolute URL in axios Requests
Summary
axios is a promise based HTTP client for the browser and node.js. The issue occurs when passing absolute URLs rather than protocol-relative URLs to axios. Even if baseURL is set, axios sends the request to the specified absolute URL, potentially causing SSRF and credential leakage. This issue impacts both server-side and client-side usage of axios. This issue is fixed in 1.8.2.
Severity
SSVC
Exploitation: poc
Automatable: yes
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-03-07 19:32 UTC
CWE
- CWE-918 - Server-Side Request Forgery (SSRF)
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://github.com/axios/axios/security/advisorie… | x_refsource_CONFIRM |
| https://github.com/axios/axios/issues/6463 | x_refsource_MISC |
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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.
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.
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