CVE-2025-10975 in vlarl
Summary
by MITRE • 09/26/2025
A vulnerability was found in GuanxingLu vlarl up to 31abc0baf53ef8f5db666a1c882e1ea64def2997. This vulnerability affects the function experiments.robot.bridge.reasoning_server::run_reasoning_server of the file experiments/robot/bridge/reasoning_server.py of the component ZeroMQ. Performing manipulation of the argument Message results in deserialization. Remote exploitation of the attack is possible. The exploit has been made public and could be used. This product follows a rolling release approach for continuous delivery, so version details for affected or updated releases are not provided.
If you want to get the best quality for vulnerability data then you always have to consider VulDB.
Analysis
by VulDB Data Team • 09/26/2025
The vulnerability identified as CVE-2025-10975 resides within the GuanxingLu vlarl software ecosystem, specifically targeting the ZeroMQ component through a critical flaw in the experiments/robot/bridge/reasoning_server.py file. This issue manifests in the experiments.robot.bridge.reasoning_server::run_reasoning_server function where improper handling of Message arguments creates a dangerous deserialization pathway. The vulnerability represents a significant security weakness that directly impacts the software's ability to process external inputs safely, creating potential attack vectors for malicious actors seeking to compromise the system.
The technical flaw constitutes a deserialization vulnerability that allows attackers to manipulate the Message argument passed to the reasoning server function. When the system processes this manipulated input, it executes arbitrary code through the deserialization mechanism, effectively bypassing normal security controls. This type of vulnerability falls under CWE-502 which specifically addresses deserialization of untrusted data, a well-documented weakness that has been exploited in numerous high-profile security incidents. The vulnerability's classification aligns with ATT&CK technique T1203 which describes exploitation of software vulnerabilities through deserialization attacks, making it particularly dangerous in environments where the software processes external communications.
Remote exploitation capabilities make this vulnerability especially concerning as attackers can leverage it from outside the local network without requiring physical access or prior authentication. The public availability of exploit code significantly increases the risk level, as it removes the barrier to entry for potential attackers who may not possess advanced technical skills. The rolling release approach adopted by this software product complicates remediation efforts since version information is not consistently maintained, making it difficult for users to determine their exposure status or identify when patches became available. This approach to software delivery, while beneficial for continuous updates, creates challenges for vulnerability management and security assessment.
The operational impact of this vulnerability extends beyond immediate code execution capabilities to encompass potential system compromise, data exfiltration, and persistent access to affected systems. Attackers could leverage this vulnerability to establish backdoors, escalate privileges, or deploy additional malicious payloads within the network. Organizations using this software must consider the broader implications of a compromised system, including potential lateral movement opportunities and the possibility of cascading attacks throughout their infrastructure. The vulnerability's presence in a reasoning server component suggests potential impacts on artificial intelligence decision-making processes, which could lead to unauthorized system behavior or manipulation of automated responses.
Mitigation strategies should prioritize immediate patching of affected systems, implementation of network segmentation to limit attack surface, and deployment of intrusion detection systems to monitor for exploitation attempts. Organizations should also consider implementing input validation controls, restricting network access to the reasoning server component, and establishing monitoring protocols for unusual deserialization activities. The public availability of exploit code necessitates urgent action, as the window for potential exploitation is likely already open. Security teams should conduct comprehensive vulnerability assessments across all systems running affected versions and implement compensating controls while awaiting official patches. Regular security audits and penetration testing should be performed to identify similar vulnerabilities in related components, as the presence of one deserialization vulnerability often indicates potential for additional weaknesses in the software architecture.