CVE-2025-6242 — Vllm: server side request forgery (ssrf) in mediaconnector
A Server-Side Request Forgery (SSRF) vulnerability exists in the MediaConnector class within the vLLM project's multimodal feature set. The load_from_url and load_from_url_async methods fetch and process media from user-provided URLs without adequate restrictions on the target hosts. This allows an attacker to coerce the vLLM server into making arbitrary requests to internal network resources.
Who exploits it and how urgent it is
A 0–100 score combining confirmed exploitation, ransomware and APT use, public exploits, EPSS and severity.
How the score was calculated
- EPSS 0.2% (30-day exploitation probability)+2
- Technical severity (high)+6
The score orders the patch queue; it does not replace your asset context (internet exposure, criticality, compensating controls).
Triage
Is it exploited, how likely is exploitation, what does it touch, and how severe do the sources call it.
CISA SSVC assessment CISA
Three decision points CISA publishes for the CVEs it assesses (SSVC 2.0.3). A stakeholder decision, not a severity score.
Remediation
Fixes and mitigations documented by the sources — linked to the original advisory.
- Pip Vllm: fixed in version 0.11.0 — github.com (GitHub Security Advisories)
First 24 hours
Ordered from the record’s own fields — exposure first, because you cannot patch what you have not found. A rule-generated starting point.
- Identify exposed assets running the affected vendor/product/version combinations (Red Hat Ai Inference Server, Red Hat Red Hat Enterprise Linux AI (RHEL AI), Pip Vllm).
- Apply the fixed version or the patch documented under Remediation.
- Search available logs for exploit probes matching the vulnerability class (ssrf), errors and authentication anomalies.
Affected scope
Vendor, product and versions as the sources word them.
| Vendor | Product | Versions | Status | Source |
|---|---|---|---|---|
| Red Hat | Ai Inference Server | versions not specified by the source | Vulnerable | CVE Program (cve.org) e CISA ADP |
| Red Hat | Red Hat Enterprise Linux AI (RHEL AI) | versions not specified by the source | Vulnerable | CVE Program (cve.org) e CISA ADP |
| Pip | Vllm | ≥ 0.5.0 and < 0.11.0 | Vulnerable | GitHub Security Advisories |
Attack characteristics
The CVSS vector, decoded. It describes the attack, not your exposure to it.
Every base score collected
Sources score independently and may disagree; each row says who scored it and under which version.
| Score | Version | Severity | Expl. | Impact | Scored by |
|---|---|---|---|---|---|
| 7.1 | CVSS 3.1 | High | 1.6 | 5.5 | secalert@redhat.com via NIST National Vulnerability Database |
| 7.1 | CVSS 3.1 | High | — | — | GitHub, Inc. via GitHub Security Advisories |
| 7.1 | CVSS 3.1 | High | — | — | redhat via CVE Program (cve.org) e CISA ADP |
CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:U/C:H/I:L/A:H (score shown at the top: secalert@redhat.com) Weakness MITRE
Weakness class according to MITRE’s CWE catalog.
EPSS over time FIRST
Oct 18, 2026 → Oct 19, 2026 · 2 reading(s) · max 0.25%
Detection
Read off the CVSS vector and the weakness class. Starting points, not tested rules.
- Prioritize edge telemetry for the network-reachable component (proxy, WAF, IDS/IPS).
- Monitor outbound requests from the server to internal IPs and cloud metadata endpoints.
Timeline
What happened to this CVE, newest first.
- 2026
- Fix documented
- Affected products added · pip/vllm
- Affected products added · red hat/red hat ai inference server, red hat/red hat enterprise linux ai (rhel ai)
- SSVC assessment updated
- Record published
- CVSS changed · 7.1
- Source status changed · → Deferred
- Record created in this database
- 2025
- CVE published
Source activity
Repeated readings are counted rather than listed.
- 2× References added · Oct 18, 2026 – Oct 18, 2026
- 1× Official description updated · Oct 18, 2026 – Oct 18, 2026
References
6 on the record
- github.com/advisories/GHSA-3f6c-7fw2-ppm4
- github.com/vllm-project/vllm/security/advisories/GHSA-3f6c-7fw2-ppm4
- nvd.nist.gov/vuln/detail/CVE-2025-6242
- bugzilla.redhat.com/show_bug.cgi
- github.com/vllm-project/vllm/commit/9d9a2b77f19f68262d5e469c4e82c0f6365ad72d
- access.redhat.com/security/cve/CVE-2025-6242
Elsewhere on this site
Related CVEs (same products)
- Medium CVE-2026-105760 vLLM: GLMGA video sampling permits request-driven CPU and memory exhaustion 14d
- Medium CVE-2026-73557 vLLM: Incomplete CVE-2025-62164 remediation can be bypassed by concurrent prompt parts Aug 13, 2026
- High CVE-2026-56210 Libaom: libaom: heap-buffer-overflow read via missing bounds check in ctrl_set_layer_id Jun 19, 2026
- High CVE-2026-56209 Libaom: libaom: arbitrary address write via svc layer context oob and cyclic refresh map pointer hijack Jun 19, 2026
- High CVE-2026-56208 Libaom: libaom: heap buffer overflow in av1 encoder first-pass stats buffer via lap mode Jun 19, 2026
- High CVE-2026-56211 Libaom: libaom: remote code execution via svc layer context handling with attacker-controlled frames Jun 19, 2026
Not in any source we poll
Listed rather than left blank: an empty field and an unmeasured one look identical on screen, and only one is a reason to look elsewhere.
- No confirmed IOCs, IP addresses, domains, file hashes, or malware artifacts supplied.
- No organization-specific asset inventory, compensating-control status, or patch deployment evidence supplied.
- No exploit packet captures, log samples, or incident case IDs supplied.
Answered from this record
Text assembled only from the fields above — no additional claims.
What should defenders know first?
CVE-2025-6242 is "Vllm: server side request forgery (ssrf) in mediaconnector", rated High. It affects Ai Inference Server (Red Hat); versions are in the scope table. It is not in CISA KEV, which does not rule out exploitation. CVSS 7.1 describes technical severity. EPSS 0.25% estimates the probability of exploitation in 30 days; neither replaces your asset context. First verify whether exposed or business-critical assets run the affected versions, apply vendor fixes or mitigations, and preserve logs for review.
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