BREW-AIDER-CVE-2026-10177 (CVE-2026-10177)
Vulnerability from osv_homebrew – Published: 2026-08-13 16:35 – Updated: 2026-09-09 23:40 – Source website
VLAI
Summary
Aider-AI Aider AWS EC2 Metadata Endpoint api_docs.py requests.get server-side request forgery
Details
A security vulnerability has been detected in Aider-AI Aider 0.86.3. This affects the function requests.get of the file api_docs.py of the component AWS EC2 Metadata Endpoint. The manipulation leads to server-side request forgery. The attack is possible to be carried out remotely. The exploit has been disclosed publicly and may be used. It is suggested to install a patch to address this issue. The pull request to fix this issue awaits acceptance.
Severity
References
{
"affected": [
{
"ecosystem_specific": {
"fix": null,
"range_state": "affected"
},
"package": {
"ecosystem": "Homebrew",
"name": "aider",
"purl": "pkg:brew/aider"
},
"ranges": [
{
"events": [
{
"introduced": "0"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"database_specific": {
"confidence": "high",
"source": "matched",
"strategy": "git",
"upstream_evidence": [
{
"ecosystem": "GIT",
"key": "https://github.com/aider-ai/aider",
"name": "https://github.com/aider-ai/aider",
"strategy": "git",
"subject_version": "0.86.2"
},
{
"ecosystem": "PyPI",
"key": "pkg:pypi/aider-chat@0.86.2",
"name": "aider-chat",
"strategy": "registry",
"subject_version": "0.86.2"
}
]
},
"details": "A security vulnerability has been detected in Aider-AI Aider 0.86.3. This affects the function requests.get of the file api_docs.py of the component AWS EC2 Metadata Endpoint. The manipulation leads to server-side request forgery. The attack is possible to be carried out remotely. The exploit has been disclosed publicly and may be used. It is suggested to install a patch to address this issue. The pull request to fix this issue awaits acceptance.",
"id": "BREW-aider-CVE-2026-10177",
"modified": "2026-09-09T23:40:56Z",
"published": "2026-08-13T16:35:12Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/Aider-AI/aider/"
},
{
"type": "ADVISORY",
"url": "https://github.com/CVEProject/cvelistV5/tree/main/cves/2026/10xxx/CVE-2026-10177.json"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-10177"
},
{
"type": "ADVISORY",
"url": "https://vuldb.com/cve/CVE-2026-10177"
},
{
"type": "ADVISORY",
"url": "https://vuldb.com/submit/819911"
},
{
"type": "ADVISORY",
"url": "https://vuldb.com/vuln/367458"
},
{
"type": "REPORT",
"url": "https://github.com/Aider-AI/aider/issues/5075"
},
{
"type": "REPORT",
"url": "https://vuldb.com/vuln/367458/cti"
},
{
"type": "FIX",
"url": "https://github.com/Aider-AI/aider/pull/5137"
}
],
"schema_version": "1.7.3",
"severity": [
{
"score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:L/VI:L/VA:L/SC:N/SI:N/SA:N/E:P",
"type": "CVSS_V4"
}
],
"summary": "Aider-AI Aider AWS EC2 Metadata Endpoint api_docs.py requests.get server-side request forgery",
"upstream": [
"CVE-2026-10177",
"GHSA-hchg-qm84-cj9p",
"PYSEC-2026-2336"
]
}
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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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