CVE-2025-10952VulnerabilityPublished 25 Sep 2025

CVE-2025-10952: geyang ml-logger · Information Disclosure

Affects geyang ml-loggerSource CVE Database V5

Not known to be exploited · CVSS 6.9 Medium.

Data as of 25 Oct 2026, 19:02 UTC

CVSS 4.0 base score

6.9Medium

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

EPSS: 0.5% chance of exploitation in 30 days – higher than 45% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.

CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:L/VI:N/VA:N/SC:N/SI:N/SA:N/E:P

Base metrics

Attack Vector: Network
Attack Complexity: Low
Attack Requirements: None
Privileges Required: None
User Interaction: None
Vulnerable system confidentiality: Low
Vulnerable system integrity: None
Vulnerable system availability: None
Subsequent system confidentiality: None
Subsequent system integrity: None
Subsequent system availability: None

Threat metrics

Exploit Maturity: POC

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

Attack Vector: Network: The vulnerable system is bound to the network stack and the set of possible attackers extends beyond the other options listed below, 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: The attacker must take no measurable action to exploit the vulnerability. The attack requires no target-specific circumvention to exploit the vulnerability. An attacker can expect repeatable success against the vulnerable system.
Attack Requirements: None: The successful attack does not depend on the deployment and execution conditions of the vulnerable system. The attacker can expect to be able to reach the vulnerability and execute the exploit under all or most instances of the vulnerability.
Privileges Required: None: The attacker is unauthorized prior to attack, and therefore does not require any access to settings or files of the vulnerable system to carry out an attack.
User Interaction: None: The vulnerable system can be exploited without interaction from any human user, other than the attacker.
Vulnerable system 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 Vulnerable System.
Vulnerable system integrity: None: There is no loss of integrity within the Vulnerable System.
Vulnerable system availability: None: There is no impact to availability within the Vulnerable System.
Subsequent system confidentiality: None: There is no loss of confidentiality within the Subsequent System or all confidentiality impact is constrained to the Vulnerable System.
Subsequent system integrity: None: There is no loss of integrity within the Subsequent System or all integrity impact is constrained to the Vulnerable System.
Subsequent system availability: None: There is no impact to availability within the Subsequent System or all availability impact is constrained to the Vulnerable System.
Exploit Maturity: POC: Based on threat intelligence sources each of the following must apply: · Proof-of-concept is publicly available · No knowledge of reported attempts to exploit this vulnerability · No knowledge of publicly available solutions used to simplify attempts to exploit the vulnerability

Definitions: FIRST CVSS v4.0 specification.

Exploitation evidence

Disclosed: evidence found.
CVE published 25 Sep 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-2025-10952 effectively, European organizations should: 1) Immediately audit and restrict network access to ml-logger endpoints, ensuring that stream_handler and related APIs are not exposed to untrusted networks or the internet. 2) Implement strict input validation and sanitization controls on the 'key' argument at the application or proxy level to prevent malicious manipulation. 3) Monitor logs and network traffic for unusual or unauthorized access patterns targeting ml-logger components. 4) Engage with the geyang vendor or community to obtain the latest patched versions or security advisories, given the continuous delivery model complicates version tracking. 5) Where possible, isolate ml-logger services within secure network segments and enforce strong authentication and authorization mechanisms to limit access. 6) Consider deploying Web Application Firewalls (WAFs) or API gateways with custom rules to detect and block exploit attempts targeting this vulnerability. 7) Conduct internal security reviews of ML infrastructure to identify any indirect exposure of sensitive data through logging services. 8) Prepare incident response plans to address potential data leakage scenarios stemming from this vulnerability.

Description

A security flaw has been discovered in geyang ml-logger up to acf255bade5be6ad88d90735c8367b28cbe3a743. Affected by this issue is the function stream_handler of the file ml_logger/server.py of the component File Handler. Performing manipulation of the argument key results in information disclosure. The attack can be initiated remotely. The exploit has been released to the public and may be exploited. Continious delivery with rolling releases is used by this product. Therefore, no version details of affected nor updated releases are available.

Impact

For European organizations, the information disclosure vulnerability in ml-logger could lead to unauthorized access to sensitive operational data, including machine learning model logs, telemetry, or metadata. This exposure may compromise confidentiality of proprietary algorithms, training data insights, or system behavior patterns, potentially undermining competitive advantage or violating data protection regulations such as GDPR if personal data is indirectly exposed. While the vulnerability does not directly impact system integrity or availability, the leaked information could be leveraged by attackers for further targeted attacks or reconnaissance. Organizations relying on ml-logger for critical AI/ML workflows in sectors like finance, healthcare, or manufacturing may face increased risk of intellectual property theft or compliance violations. The remote, unauthenticated nature of the exploit increases the attack surface, particularly for deployments with exposed or poorly segmented logging endpoints. The lack of clear patch availability due to continuous delivery complicates timely remediation, potentially prolonging exposure.

References

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Exploitation evidence

What to do

Description

AI analysisMachine-generated, updated 25 Sep 2025, 15:39 UTC

Technical details

References

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