Description
BerriAI/litellm version v1.35.8 contains a vulnerability where an attacker can achieve remote code execution. The vulnerability exists in the `add_deployment` function, which decodes and decrypts environment variables from base64 and assigns them to `os.environ`. An attacker can exploit this by sending a malicious payload to the `/config/update` endpoint, which is then processed and executed by the server when the `get_secret` function is triggered. This requires the server to use Google KMS and a database to store a model.
EPSS Score:
2%
Comprehensive Technical Analysis of EUVD-2024-2057
1. Vulnerability Assessment and Severity Evaluation
The vulnerability in BerriAI/litellm version v1.35.8 allows for remote code execution (RCE) due to improper handling of environment variables in the add_deployment function. The base score of 9.8, as per CVSS v3.0, indicates a critical severity level. The vector CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H highlights the following:
- Attack Vector (AV): Network (N) - The vulnerability is exploitable over the network.
- Attack Complexity (AC): Low (L) - The attack requires minimal complexity.
- Privileges Required (PR): None (N) - No privileges are required to exploit the vulnerability.
- User Interaction (UI): None (N) - No user interaction is required.
- Scope (S): Unchanged (U) - The vulnerability does not change the security scope.
- Confidentiality (C): High (H) - The vulnerability has a high impact on confidentiality.
- Integrity (I): High (H) - The vulnerability has a high impact on integrity.
- Availability (A): High (H) - The vulnerability has a high impact on availability.
2. Potential Attack Vectors and Exploitation Methods
An attacker can exploit this vulnerability by sending a malicious payload to the /config/update endpoint. The payload is processed by the add_deployment function, which decodes and decrypts environment variables from base64 and assigns them to os.environ. This can lead to the execution of arbitrary code on the server. The attack requires the server to use Google KMS and a database to store a model, which suggests that the attacker needs to understand the server's configuration to some extent.
3. Affected Systems and Software Versions
The vulnerability affects BerriAI/litellm version v1.35.8. It is crucial to note that any system running this version of the software and utilizing the add_deployment function is at risk. The ENISA ID Product specifies that all versions up to the latest are potentially affected unless explicitly patched.
4. Recommended Mitigation Strategies
- Immediate Patching: Upgrade to a patched version of BerriAI/litellm as soon as it is available.
- Input Validation: Implement strict input validation and sanitization for the
/config/updateendpoint to prevent malicious payloads. - Environment Variable Handling: Ensure that environment variables are handled securely and avoid direct assignment to
os.environwithout proper validation. - Network Segmentation: Isolate critical systems and limit network access to the
/config/updateendpoint. - Monitoring and Logging: Enhance monitoring and logging to detect and respond to any suspicious activities related to the
add_deploymentfunction.
5. Impact on European Cybersecurity Landscape
The high severity of this vulnerability poses a significant risk to organizations using BerriAI/litellm, 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 unauthorized access, which can have severe implications for data privacy and operational continuity.
6. Technical Details for Security Professionals
- Vulnerable Function: The
add_deploymentfunction in BerriAI/litellm v1.35.8. - Exploitation Path: The attacker sends a malicious payload to the
/config/updateendpoint, which is processed by theadd_deploymentfunction. The function decodes and decrypts environment variables from base64 and assigns them toos.environ, leading to RCE. - Dependencies: The server must use Google KMS and a database to store a model for the vulnerability to be exploited.
- References:
Conclusion
The vulnerability in BerriAI/litellm v1.35.8 is critical and requires immediate attention. Organizations should prioritize patching and implementing robust security measures to mitigate the risk of exploitation. The European cybersecurity landscape must remain vigilant against such high-impact vulnerabilities to protect sensitive data and ensure operational continuity.