Description
vllm-project vllm version 0.6.0 contains a vulnerability in the AsyncEngineRPCServer() RPC server entrypoints. The core functionality run_server_loop() calls the function _make_handler_coro(), which directly uses cloudpickle.loads() on received messages without any sanitization. This can result in remote code execution by deserializing malicious pickle data.
EPSS Score:
0%
Comprehensive Technical Analysis of EUVD-2025-6874
1. Vulnerability Assessment and Severity Evaluation
The vulnerability described in EUVD-2025-6874 affects the vllm-project's vllm version 0.6.0. Specifically, the AsyncEngineRPCServer() RPC server entrypoints are vulnerable due to the use of cloudpickle.loads() without proper sanitization of received messages. This can lead to remote code execution (RCE) when deserializing malicious pickle data.
Severity Evaluation:
- Base Score: 9.8 (Critical)
- Base Score Version: CVSS 3.0
- Base Score Vector: CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
The high base score indicates a critical vulnerability due to the following factors:
- Attack Vector (AV): Network (N)
- Attack Complexity (AC): Low (L)
- Privileges Required (PR): None (N)
- User Interaction (UI): None (N)
- Scope (S): Unchanged (U)
- Confidentiality (C): High (H)
- Integrity (I): High (H)
- Availability (A): High (H)
2. Potential Attack Vectors and Exploitation Methods
Attack Vectors:
- Network-Based Attacks: An attacker can exploit this vulnerability over the network by sending crafted pickle data to the
AsyncEngineRPCServer()RPC server entrypoints. - Malicious Insiders: Internal users with network access to the RPC server could also exploit this vulnerability.
Exploitation Methods:
- Deserialization Attack: The attacker sends a specially crafted pickle payload that, when deserialized by
cloudpickle.loads(), executes arbitrary code on the server. - Payload Crafting: The payload can be designed to perform various malicious actions, such as data exfiltration, system compromise, or further propagation within the network.
3. Affected Systems and Software Versions
Affected Software:
vllm-project'svllmversion 0.6.0
Affected Systems:
- Any system running the vulnerable version of
vllmwith theAsyncEngineRPCServer()RPC server entrypoints exposed to the network.
4. Recommended Mitigation Strategies
Immediate Mitigation:
- Patching: Upgrade to a patched version of
vllmthat addresses this vulnerability. - Network Segmentation: Isolate the RPC server from untrusted networks to limit exposure.
- Input Validation: Implement strict input validation and sanitization for all incoming data to the RPC server.
Long-Term Mitigation:
- Code Review: Conduct a thorough code review to identify and mitigate similar deserialization vulnerabilities.
- Security Training: Educate developers on secure coding practices, especially regarding deserialization and input handling.
- Regular Updates: Ensure that all software dependencies are regularly updated to their latest, secure versions.
5. Impact on European Cybersecurity Landscape
The vulnerability poses a significant risk to organizations using the vllm software, particularly those in critical sectors such as finance, healthcare, and government. The potential for remote code execution can lead to data breaches, service disruptions, and loss of sensitive information. Given the critical nature of the vulnerability, it is essential for European organizations to prioritize patching and implementing robust security measures to mitigate the risk.
6. Technical Details for Security Professionals
Vulnerability Details:
- The vulnerability arises from the use of
cloudpickle.loads()without proper sanitization in the_make_handler_coro()function, which is called byrun_server_loop(). - The
cloudpicklelibrary is used for serializing and deserializing Python objects, but it does not inherently provide security against malicious data.
Detection and Monitoring:
- Logging: Implement comprehensive logging for all RPC server interactions to detect and analyze suspicious activities.
- Intrusion Detection Systems (IDS): Deploy IDS to monitor network traffic for signs of exploitation attempts.
- Anomaly Detection: Use anomaly detection tools to identify unusual patterns in RPC server traffic.
Incident Response:
- Containment: Immediately isolate affected systems to prevent further spread of the attack.
- Forensic Analysis: Conduct a detailed forensic analysis to understand the scope and impact of the attack.
- Recovery: Restore affected systems from clean backups and apply necessary patches.
References:
By addressing this vulnerability promptly and implementing robust security measures, organizations can significantly reduce the risk of exploitation and protect their systems from potential attacks.