Description
A remote code execution vulnerability exists in open-mmlab/mmdetection version v3.3.0. The vulnerability is due to the use of the `pickle.loads()` function in the `all_reduce_dict()` distributed training API without proper sanitization. This allows an attacker to execute arbitrary code by broadcasting a malicious payload to the distributed training network.
EPSS Score:
0%
Comprehensive Technical Analysis of EUVD-2025-7029
1. Vulnerability Assessment and Severity Evaluation
The vulnerability described in EUVD-2025-7029 is a remote code execution (RCE) flaw in the open-mmlab/mmdetection software, specifically in version v3.3.0. The issue arises from the improper use of the pickle.loads() function within the all_reduce_dict() distributed training API. This function deserializes data without proper sanitization, allowing an attacker to execute arbitrary code.
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 CVSS score of 9.8 indicates a critical vulnerability. The attack vector (AV:N) is network-based, requiring low complexity (AC:L) and no privileges (PR:N) or user interaction (UI:N). The impact on confidentiality, integrity, and availability is high (C:H/I:H/A:H), making this a severe threat.
2. Potential Attack Vectors and Exploitation Methods
Attack Vectors:
- Network-Based Attack: An attacker can exploit this vulnerability by broadcasting a malicious payload to the distributed training network.
- Malicious Insider: An insider with access to the training network could inject malicious data.
Exploitation Methods:
- Payload Crafting: An attacker crafts a malicious payload that, when deserialized by
pickle.loads(), executes arbitrary code. - Network Broadcast: The attacker broadcasts this payload to the distributed training network, affecting all nodes that process the malicious data.
3. Affected Systems and Software Versions
Affected Software:
open-mmlab/mmdetectionversion v3.3.0
Affected Systems:
- Any system running the vulnerable version of
open-mmlab/mmdetectionin a distributed training environment.
4. Recommended Mitigation Strategies
Immediate Mitigation:
- Patching: Upgrade to a patched version of
open-mmlab/mmdetectionthat addresses this vulnerability. - Network Segmentation: Isolate the distributed training network to limit the attack surface.
- Input Validation: Implement strict input validation and sanitization for data processed by
pickle.loads().
Long-Term Mitigation:
- Code Review: Conduct a thorough code review to identify and mitigate similar vulnerabilities.
- Security Training: Educate developers on secure coding practices, especially regarding deserialization.
- Regular Updates: Ensure that all software components are regularly updated to the latest secure versions.
5. Impact on European Cybersecurity Landscape
The vulnerability poses a significant risk to organizations using open-mmlab/mmdetection for distributed training, particularly in sectors such as research, academia, and industries relying on machine learning models. The potential for remote code execution can lead to data breaches, unauthorized access, and disruption of critical services. This underscores the need for robust cybersecurity measures and continuous monitoring of open-source software dependencies.
6. Technical Details for Security Professionals
Vulnerability Details:
- Function:
pickle.loads() - API:
all_reduce_dict() - Issue: Lack of proper sanitization during deserialization.
Detection and Monitoring:
- Log Analysis: Monitor logs for unusual activity related to the
all_reduce_dict()API. - Network Traffic: Analyze network traffic for anomalous patterns that may indicate a malicious payload.
- Intrusion Detection: Implement intrusion detection systems (IDS) to detect and alert on suspicious activities.
Response and Recovery:
- Incident Response: Have an incident response plan in place to quickly address any detected exploitation.
- Backup and Restore: Ensure regular backups and have a recovery plan to restore systems to a known good state.
References:
By addressing this vulnerability promptly and implementing robust security measures, organizations can mitigate the risk of exploitation and protect their critical assets.