Description
BentoML Allows Remote Code Execution (RCE) via Insecure Deserialization
EPSS Score:
0%
Comprehensive Technical Analysis of EUVD-2025-9752
1. Vulnerability Assessment and Severity Evaluation
The vulnerability EUVD-2025-9752, also known as CVE-2025-27520, pertains to a Remote Code Execution (RCE) flaw in BentoML due to insecure deserialization. The CVSS (Common Vulnerability Scoring System) base score of 9.8 indicates a critical severity level. The scoring vector CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H breaks down as follows:
- Attack Vector (AV): Network (N) - The vulnerability is exploitable over the network.
- Attack Complexity (AC): Low (L) - The attack requires minimal skill or resources.
- Privileges Required (PR): None (N) - No special privileges are needed to exploit the vulnerability.
- User Interaction (UI): None (N) - No user interaction is required.
- Scope (S): Unchanged (U) - The vulnerability does not affect resources beyond the security scope managed by the security authority.
- Confidentiality (C): High (H) - Complete loss of confidentiality.
- Integrity (I): High (H) - Complete loss of integrity.
- Availability (A): High (H) - Complete loss of availability.
This high score underscores the critical nature of the vulnerability, making it a top priority for immediate remediation.
2. Potential Attack Vectors and Exploitation Methods
The primary attack vector for this vulnerability is the network, allowing remote attackers to exploit it without needing any special privileges or user interaction. The insecure deserialization flaw can be exploited by sending crafted serialized data to the BentoML application, which, when deserialized, can execute arbitrary code.
Potential exploitation methods include:
- Network-Based Attacks: Attackers can send malicious serialized data over the network to the BentoML application.
- Man-in-the-Middle (MitM) Attacks: Intercepting and modifying serialized data in transit to include malicious payloads.
- Phishing and Social Engineering: Tricking users into interacting with malicious serialized data.
3. Affected Systems and Software Versions
The vulnerability affects BentoML versions 1.3.4 and earlier, as well as versions before 1.4.3. Organizations using these versions are at risk and should prioritize updating to a patched version.
4. Recommended Mitigation Strategies
To mitigate the risk associated with this vulnerability, the following strategies are recommended:
- Immediate Patching: Upgrade to BentoML version 1.4.3 or later, which includes the security fix.
- Input Validation: Implement robust input validation and sanitization to ensure that only expected data formats are processed.
- Deserialization Security: Use secure deserialization libraries and practices to prevent the execution of arbitrary code.
- Network Security: Implement network security measures such as firewalls, intrusion detection systems (IDS), and intrusion prevention systems (IPS) to monitor and block suspicious traffic.
- Regular Audits: Conduct regular security audits and vulnerability assessments to identify and address potential security weaknesses.
5. Impact on European Cybersecurity Landscape
The critical nature of this vulnerability poses a significant risk to organizations across Europe, particularly those relying on BentoML for machine learning model deployment. The potential for RCE can lead to data breaches, unauthorized access, and service disruptions, impacting confidentiality, integrity, and availability. This underscores the need for robust cybersecurity practices and timely patch management to protect against such threats.
6. Technical Details for Security Professionals
For security professionals, the following technical details are pertinent:
- Vulnerability Type: Insecure Deserialization leading to Remote Code Execution (RCE).
- Affected Component: The deserialization mechanism in BentoML.
- Exploitation: Crafted serialized data can be sent to the application, leading to arbitrary code execution upon deserialization.
- Detection: Monitor network traffic for unusual serialized data patterns and implement logging for deserialization processes.
- Response: In case of an incident, isolate affected systems, analyze logs for indicators of compromise, and apply the necessary patches.
References:
By addressing this vulnerability promptly and comprehensively, organizations can significantly reduce the risk of exploitation and maintain the integrity and security of their systems.