VulnerabilityPublished 25 Sep 2025

CVE-2025-10950: geyang ml-logger · Deserialization

Affects geyang ml-loggerSource CVE Database V5

Public exploit available · CVSS 5.3 Medium.

Data as of 26 Oct 2026, 10:12 UTC

CVSS 4.0 base score

5.3Medium

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

EPSS: 0.3% chance of exploitation in 30 days – higher than 33% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.

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

Base metrics

Attack Vector: Network
Attack Complexity: Low
Attack Requirements: None
Privileges Required: Low
User Interaction: None
Vulnerable system confidentiality: Low
Vulnerable system integrity: Low
Vulnerable system availability: Low
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: Low: The attacker requires privileges that provide basic capabilities that are typically limited to settings and resources owned by a single low-privileged user. Alternatively, an attacker with Low privileges has the ability to access only non-sensitive resources.
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: Low: Modification of data is possible, but the attacker does not have control over the consequence of a modification, or the amount of modification is limited. The data modification does not have a direct, serious impact to the Vulnerable System.
Vulnerable system availability: Low: Performance is reduced or there are interruptions in resource availability. Even if repeated exploitation of the vulnerability is possible, the attacker does not have the ability to completely deny service to legitimate users. The resources in the Vulnerable System are either partially available all of the time, or fully available only some of the time, but overall there is no direct, serious consequence to 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: evidence found.
CVSS exploit maturity: proof of concept public
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), CVSS vector, 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

AI-suggested

To mitigate CVE-2025-10950, European organizations should implement the following specific measures: 1) Immediately audit and monitor all instances of ml-logger deployments to identify exposure to the vulnerable log_handler function. 2) Apply strict input validation and sanitization on all data passed to the logging components to prevent malicious deserialization payloads. 3) If possible, isolate the ml-logger service within a segmented network zone with restricted access to limit attack surface. 4) Employ runtime application self-protection (RASP) or application-layer firewalls to detect and block suspicious deserialization attempts. 5) Engage with the vendor or community to track rolling release updates closely and prioritize applying any patches or mitigations as soon as they become available. 6) Implement robust logging and anomaly detection to identify unusual activity related to logging services. 7) Consider replacing or supplementing ml-logger with alternative logging solutions that do not rely on unsafe deserialization practices until a fix is available. 8) Educate development and DevOps teams about secure coding practices around serialization and deserialization to prevent similar vulnerabilities in the future.

Description

A vulnerability was determined in geyang ml-logger up to acf255bade5be6ad88d90735c8367b28cbe3a743. Affected is the function log_handler of the file ml_logger/server.py of the component Ping Handler. This manipulation of the argument data causes deserialization. It is possible to initiate the attack remotely. The exploit has been publicly disclosed and may be utilized. This product is using a rolling release to provide continious delivery. Therefore, no version details for affected nor updated releases are available.

Impact

For European organizations, the impact of CVE-2025-10950 can be significant, especially for those relying on ml-logger for machine learning model monitoring, logging, and operational analytics. Exploitation could allow attackers to execute arbitrary code remotely, leading to unauthorized access, data manipulation, or disruption of ML workflows. This could compromise the integrity of ML model training and inference logs, affecting decision-making processes and compliance with data governance regulations such as GDPR. Additionally, disruption or manipulation of logging could hinder incident detection and response capabilities. Organizations in sectors with heavy ML adoption—such as finance, healthcare, automotive, and manufacturing—may face operational risks and reputational damage. The rolling release nature of the product complicates patching and vulnerability management, potentially prolonging exposure. However, the medium severity and limited scope of impact suggest that while the threat is real, it may not lead to widespread catastrophic failures unless combined with other vulnerabilities or poor security controls.

References

Community reviews

Loading reviews…

Exploitation evidence

What to do

Description

AI analysisMachine-generated, updated 25 Sep 2025, 14:40 UTC

Technical details

References

Community reviews

Related threats