CVE-2026-17534 (GCVE-0-2026-17534)
Vulnerability from cvelistv5 – Published: 2026-07-27 09:19 – Updated: 2026-07-27 10:24
VLAI
EPSS
VEX
Title
Kimi Code FetchURL SSRF protection bypass via DNS-resolving hostnames and redirects
Summary
Kimi Code (@moonshot-ai/kimi-code) before 0.27.0 implements FetchURL SSRF hardening as a static hostname and IP-literal denylist in assertSafeFetchTarget, without resolving DNS or re-validating hosts after HTTP redirects. An attacker who can influence a FetchURL call (for example via prompt injection) can supply a crafted public hostname that resolves to loopback or another internal address, or a public URL that redirects to such a target, and thereby reach internal network services that the denylist was intended to block. FetchURL is included in the default auto-approve tool set, so the call does not require interactive user confirmation in manual mode.
Severity
5.5 (Medium)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-07-27 10:23 UTC
CWE
- CWE-918 - Server-Side Request Forgery (SSRF)
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://github.com/MoonshotAI/kimi-code/commit/31… | patch |
| https://github.com/MoonshotAI/kimi-code/pull/1791 | patch |
| https://github.com/MoonshotAI/kimi-code/releases/… | vendor-advisory |
| https://github.com/MoonshotAI/kimi-code | product |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| MoonshotAI | Kimi Code |
Affected:
0 , < 0.27.0
(semver)
|
guessed |
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vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
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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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