Listen to this Post
How CVE-2026-59820 Works
LiteLLM is a popular AI gateway proxy server that provides a standardized, OpenAI-compatible interface for calling over 100 different Large Language Model (LLM) APIs. Prior to version 1.83.7-stable, a path traversal vulnerability existed in the processing of uploaded skill ZIP archives.
The core of the issue lies in the insufficient validation of file paths during the extraction of these skill archives. An authenticated user with specific permissions—such as access to the /v1/skills, anthropic_routes, or llm_api_routes—could upload a maliciously crafted ZIP file. This archive would contain path traversal sequences (e.g., ../) in its file paths. When the LiteLLM server extracted the archive, it failed to properly sanitize these paths, allowing files to be written outside the intended extraction or staging directory.
This vulnerability enables a remote, authenticated attacker to write arbitrary files to any location on the server’s filesystem that the LiteLLM process has write access to. The ability to write arbitrary files is a significant security risk, as it can be leveraged to achieve remote code execution (RCE). For example, an attacker could overwrite critical system files, plant malicious scripts, or place a `.pth` file in Python’s site-packages directory to execute code automatically upon Python startup. The attack vector is remote and requires low privileges, making it a serious concern for any deployed instance of LiteLLM.
The vulnerability is fixed in version 1.83.7-stable and later. Users are strongly advised to upgrade immediately to mitigate the risk.
DailyCVE Form
Platform: LiteLLM
Version: < 1.83.7
Vulnerability: Path Traversal
Severity: Medium
Date: 2026-07-08
Prediction: 2026-07-13
What Undercode Say
The path traversal vulnerability in LiteLLM’s skill extraction is a classic example of how failing to validate archive contents can lead to severe consequences. This is not just an arbitrary file write; it is a gateway to full server compromise. Attackers can chain this with other techniques to achieve remote code execution, as seen in recent supply chain attacks where `.pth` files were used to execute malicious code on Python startup. The following analytics and commands illustrate the potential impact and how to detect such an attack.
Analytics
Check for suspicious outbound connections from the LiteLLM server sudo netstat -tunap | grep ESTABLISHED | grep python Monitor for unusual file writes in sensitive directories sudo auditctl -w /etc/ -p wa -k litellm_write sudo auditctl -w /usr/local/lib/python3.x/dist-packages/ -p wa -k litellm_write Search for newly created .pth files that could indicate backdoor installation find / -name ".pth" -mtime -1 2>/dev/null Review LiteLLM logs for skill uploads with suspicious paths grep -i "skill" /var/log/litellm.log | grep -i ".."
Code Snippet
The following Python script demonstrates how an attacker could craft a malicious ZIP archive containing a path traversal payload. This script creates a file named `evil.py` in the `/tmp/` directory on the target server.
import zipfile
import os
Create a malicious ZIP file
with zipfile.ZipFile('malicious_skill.zip', 'w') as zipf:
Path traversal payload: write to /tmp/evil.py
zipf.writestr('../../../../tmp/evil.py', 'print("This is a malicious file!")')
Add a legitimate-looking file to avoid suspicion
zipf.writestr('skill_manifest.json', '{"name": "legit_skill"}')
print("Malicious ZIP archive created: malicious_skill.zip")
Exploit
To exploit CVE-2026-59820, an attacker must have valid authentication credentials that grant access to the vulnerable routes. The exploitation steps are as follows:
1. Craft a Malicious Archive: Create a ZIP file containing files with path traversal sequences (e.g., ../../../../etc/passwd).
2. Upload the Archive: Use the LiteLLM API to upload the crafted skill archive to the `/v1/skills` endpoint.
3. Trigger Extraction: The server will automatically extract the archive, writing the files to the specified traversed paths.
4. Achieve Code Execution: Overwrite a critical file like `/etc/cron.d/` or place a malicious `.pth` file in Python’s site-packages to execute code.
Protection
The primary and most effective protection against this vulnerability is to upgrade LiteLLM to version 1.83.7-stable or later. If an immediate upgrade is not possible, the following mitigations should be considered:
Restrict API Access: Limit access to the /v1/skills, anthropic_routes, and `llm_api_routes` to only trusted users and services.
Network Segmentation: Isolate the LiteLLM server from critical internal networks to limit the potential impact of a compromise.
Input Validation: Implement additional validation on the server side to sanitize file paths before extraction, though this is effectively what the patch provides.
Monitoring: Actively monitor logs for suspicious skill uploads and file system changes in sensitive directories (e.g., /etc/, /usr/local/lib/).
Impact
Successful exploitation of CVE-2026-59820 allows an authenticated attacker to write arbitrary files to the server’s filesystem. This can lead to:
Remote Code Execution (RCE): By overwriting system files or planting malicious scripts that are executed by other processes.
Privilege Escalation: Overwriting binaries or configuration files to gain higher privileges.
Data Breach: Exfiltrating sensitive data by modifying system files or planting data-stealing code.
Denial of Service (DoS): Corrupting critical system files to render the server or service inoperable.
Supply Chain Compromise: As seen in previous attacks, malicious actors can use such vulnerabilities to backdoor the software supply chain.
🎯Let’s Practice Exploiting & Learn Patching For Free:
🎓 Live Courses & Certifications:
Join Undercode Academy for Verified Certifications
🚀 Request a Custom Project:
Secure, high-velocity infrastructure and disruptive technological engineering. Contact our engineering team for high-tier development and proprietary systems:
[email protected]
💎 Smart Architecture | 🛡️ Secure by Design | ⭐ Trusted by Thousands
Sources:
Reported By: nvd.nist.gov
Extra Source Hub:
Undercode
