CVE-2025-6920VulnerabilityPublished 1 Jul 2025Updated 20 Nov 2025

CVE-2025-6920: Red Hat AI Inference Server · Missing Authentication

Affects Red Hat AI Inference ServerSource CVE Database V5

Not known to be exploited · CVSS 5.3 Medium.

Data as of 25 Oct 2026, 19:02 UTC

CVSS 3.1 base score

5.3Medium

How severe the flaw is on the 0–10 CVSS scale.

EPSS: 0.2% chance of exploitation in 30 days – higher than 21% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.

CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:N/A:N

Attack Vector: Network
Attack Complexity: Low
Privileges Required: None
User Interaction: None
Scope: Unchanged
Confidentiality: Low
Integrity: None
Availability: None

Bold values are the worst case: easiest to exploit or largest impact.

Attack Vector: Network: The vulnerable component is bound to the network stack and the set of possible attackers extends beyond the other options listed, up to and including the entire Internet. Such a vulnerability is often termed 'remotely exploitable' and can be thought of as an attack being exploitable at the protocol level one or more network hops away (e.g., across one or more routers).
Attack Complexity: Low: Specialized access conditions or extenuating circumstances do not exist. An attacker can expect repeatable success against the vulnerable component.
Privileges Required: None: The attacker is unauthorized prior to attack, and therefore does not require any access to settings or files to carry out an attack.
User Interaction: None: The vulnerable system can be exploited without any interaction from any user.
Scope: Unchanged: An exploited vulnerability can only affect resources managed by the same security authority. In this case, the vulnerable component and the impacted component are either the same, or both are managed by the same security authority.
Confidentiality: Low: There is some loss of confidentiality. Access to some restricted information is obtained, but the attacker does not have control over what information is obtained, or the amount or kind of loss is limited. The information disclosure does not cause a direct, serious loss to the impacted component.
Integrity: None: There is no loss of integrity within the impacted component.
Availability: None: There is no impact to availability within the impacted component.

Definitions: FIRST CVSS v3.1 specification.

Exploitation evidence

Disclosed: evidence found.
CVE published 1 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 2026-10-25.

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 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

AI-suggested

To mitigate CVE-2025-6920, organizations should first verify if their deployment of Red Hat AI Inference Server includes the vulnerable POST /invocations endpoint. Immediate steps include implementing network-level access controls such as IP whitelisting or VPN restrictions to limit exposure of the AI inference API to trusted users only. If patches become available from Red Hat, applying them promptly is critical. In the absence of official patches, organizations can implement API gateway solutions or reverse proxies that enforce API key validation on all endpoints, including /invocations, to ensure consistent authentication. Regularly auditing API endpoint configurations and access logs can help detect unauthorized access attempts. Additionally, segregating AI inference servers within secure network segments and employing strong monitoring and alerting for anomalous API usage patterns will reduce risk. Developers should review and update security policies to mandate authentication enforcement on all critical API functions. Finally, educating operational teams about this vulnerability and its implications will improve incident response readiness.

Description

A flaw was found in the authentication enforcement mechanism of a model inference API in ai-inference-server. All /v1/* endpoints are expected to enforce API key validation. However, the POST /invocations endpoint failed to do so, resulting in an authentication bypass. This vulnerability allows unauthorized users to access the same inference features available on protected endpoints, potentially exposing sensitive functionality or allowing unintended access to backend resources.

Impact

The primary impact of CVE-2025-6920 is unauthorized access to AI inference functionalities that should be restricted, potentially exposing sensitive data derived from AI models or allowing attackers to misuse backend AI resources. Organizations relying on the Red Hat AI Inference Server for critical AI workloads risk confidentiality breaches if attackers leverage this flaw. While the vulnerability does not affect data integrity or system availability, unauthorized inference requests could lead to information leakage or unintended operational consequences. This could undermine trust in AI services, lead to exposure of proprietary models or data, and potentially facilitate further attacks if backend systems are indirectly affected. The ease of exploitation (no authentication or user interaction required) and remote accessibility increase the likelihood of exploitation attempts, especially in environments with internet-facing AI inference endpoints. The lack of known active exploits reduces immediate risk but does not eliminate the threat, especially as awareness grows. Organizations worldwide using Red Hat AI Inference Server in sectors such as technology, finance, healthcare, and government could face reputational damage and compliance issues if sensitive AI data is exposed.

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Description

AI analysisMachine-generated, updated 26 Feb 2026, 15:54 UTC

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