CVE-2025-47277VulnerabilityPublished 7 Jul 2025Updated 6 Sep 2026

Red Hat Security Advisory: Red Hat AI Inference Server 3.0 (CUDA)

Affects Red HatSource GCVE Database

Not known to be exploited.

Data as of 26 Oct 2026, 17:16 UTC

Severity

Medium

Rated by Threat Radar · No CVSS score from NVD or the CNA

EPSS: 0.9% chance of exploitation in 30 days – higher than 61% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.

Exploitation evidence

Disclosed: evidence found.
CVE published 7 Jul 2025
Detectable: no evidence on record.
No public nuclei template indexed
Weaponised: no evidence on record.
No public exploit code indexed in Metasploit or Exploit-DB
Exploited in the wildExploited: no evidence on record.
Not in CISA KEV and no source reports exploitation
Observed by OffSeq MirageSeen by Mirage: no evidence on record.
No attack traffic recorded by OffSeq Mirage sensors

A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 26 Oct 2026.

Disclosed: The CVE record is public on cve.org. Its publication is t = 0.
Detectable: A public nuclei template can find exposed instances, for defenders and attackers alike.
Weaponised: Public exploit code exists in Metasploit or Exploit-DB, or CISA SSVC or the CVSS exploit maturity reports a public proof of concept.
Exploited in the wild: CISA KEV lists it, CISA SSVC rates exploitation active, or a source reports attacks.
Observed by OffSeq Mirage: OffSeq Mirage honeypot sensors recorded attack traffic for this CVE, or found exposed hosts without attack traffic.

Moments are spaced evenly, not to scale; a dashed line marks a gap of six months or more. Deltas count from t = 0. Sources that give only a date (CISA KEV, Exploit-DB, or a time of exactly 00:00Z) read “same day” rather than hours. Metasploit modules carry the flaw’s disclosure date, not their release date, and nuclei templates carry no date, so both appear above but not on the timeline.

What to do

Affected software

Product	Affected	Fixed in
Red Hat Product Security Red Hat	Affected >=3.0.0 Red Hat Red Hat AI Inference Server Red Hat AI Inference Server 3.1 amd64 registry.redhat.io/rhaiis/vllm-rocm-rhel9@sha256:c52ad67db7c1cc91617bd71ad1272c3a18229a4b7d9bfd0cfee2ba9a91ac4660_amd64 registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:771bc45e13c5294baa27ea8918d2ecfe7ce89d64a36344a7ee8e70b2b8254998_amd64	Fixed in–

AI-suggested

Red Hat currently does not provide a patch or mitigation that meets their standards for usability and stability. The vulnerability is mitigated by default network isolation of vLLM nodes, and the TCPStore interface should not be exposed to untrusted networks. Users should ensure that vLLM deployments follow the official documentation to restrict network exposure and avoid binding the TCPStore service to public interfaces. Monitoring and maintaining proper network segmentation is recommended until an official fix is released.

Description

Red Hat® AI Inference Server

Impact

The vulnerability could allow an attacker with direct network access to a misconfigured or publicly exposed TCPStore interface to execute arbitrary code remotely via unsafe deserialization. This impacts confidentiality, integrity, and availability of the affected system. However, the risk is constrained by the requirement for specific deployment conditions: the use of the V0 engine with PyNcclPipe, and exposure of the TCPStore service outside isolated internal networks. Under default configurations, the vulnerability is not exploitable. There are no known exploits in the wild. The overall impact is rated moderate by Red Hat.

CWE-502Deserialization of Untrusted Data (opens in a new tab)
Base weaknessLikelihood of exploit: MediumCWE Top 25 (2025): #15OWASP Top 10 A08:2025: Software or Data Integrity Failures (opens in a new tab)
The product deserializes untrusted data without sufficiently ensuring that the resulting data will be valid.
Architecture and Design · Implementation
If available, use the signing/sealing features of the programming language to assure that deserialized data has not been tainted. For example, a hash-based message authentication code (HMAC) could be used to ensure that data has not been modified.
Implementation
When deserializing data, populate a new object rather than just deserializing. The result is that the data flows through safe input validation and that the functions are safe.
Implementation
Explicitly define a final object() to prevent deserialization.
OWASP Cheat Sheets
Abuse Case (opens in a new tab)
Deserialization (opens in a new tab)
XML Security (opens in a new tab)
XML External Entity Prevention (opens in a new tab)
Software Supply Chain Security (opens in a new tab)
OWASP ASVS 4.0.3
V1.5.2 (opens in a new tab)
V5.5.3 (opens in a new tab)
V5.5.1 (opens in a new tab)
Attack patterns
Object Injection (opens in a new tab)
From MITRE CWE, MITRE CAPEC and OWASP (Top 10, OpenCRE).

References

Community reviews

Loading reviews…

Exploitation evidence

What to do

Affected software

Description

AI analysisMachine-generated, updated 16 Aug 2026, 18:18 UTC

WeaknessesCWE-502

Technical details

References

Community reviews

Related threats