CVE-2024-2057: Langchain Langchain SSRF Vulnerability

CVE-2024-2057 Overview

A critical Server-Side Request Forgery (SSRF) vulnerability has been identified in LangChain's langchain_community package version 0.0.26. The vulnerability exists in the load_local function within the TFIDFRetriever component, allowing remote attackers to manipulate server-side requests without authentication. This flaw enables attackers to potentially access internal resources, exfiltrate sensitive data, or pivot to other systems within the network.

Critical Impact

Remote attackers can exploit the SSRF vulnerability in LangChain's TFIDFRetriever to forge server-side requests, potentially accessing internal services, cloud metadata endpoints, and sensitive infrastructure not intended for public access.

Affected Products

• LangChain langchain_community version 0.0.26
• Applications utilizing the TFIDFRetriever component with load_local function
• Systems running unpatched LangChain community libraries

Discovery Timeline

• 2024-03-01 - CVE-2024-2057 published to NVD
• 2025-03-04 - Last updated in NVD database

Technical Details for CVE-2024-2057

Vulnerability Analysis

The vulnerability resides in the load_local function within libs/community/langchain_community/retrievers/tfidf.py. This function is part of the TFIDFRetriever component, which is designed to load and process local TF-IDF (Term Frequency-Inverse Document Frequency) models for document retrieval operations.

The core issue stems from insufficient validation of input parameters when loading local resources. The function fails to properly sanitize or restrict the file paths and URLs that can be processed, creating an opportunity for attackers to craft malicious inputs that cause the server to make unintended requests to arbitrary destinations.

Due to the network-accessible nature of this vulnerability and the lack of authentication requirements, attackers can exploit this flaw remotely with minimal complexity. Successful exploitation could result in unauthorized access to internal services, cloud provider metadata endpoints (such as AWS IMDSv1), or other sensitive infrastructure components that should not be externally accessible.

Root Cause

The root cause is improper input validation in the load_local function of the TFIDFRetriever class (CWE-918: Server-Side Request Forgery). The function does not adequately validate or sanitize user-supplied input before using it to construct server-side requests. This allows attackers to inject malicious URLs or file paths that redirect the server's requests to unintended targets, bypassing network security controls and accessing resources that should be protected.

Attack Vector

The attack vector is network-based, requiring no authentication or user interaction. An attacker can exploit this vulnerability remotely by providing specially crafted input to the load_local function. The manipulation causes the server to make requests on behalf of the attacker, potentially targeting:

• Internal network services not exposed to the internet
• Cloud metadata services (AWS, GCP, Azure metadata endpoints)
• Local file system resources
• Other backend services accessible from the vulnerable server

The exploit has been publicly disclosed and a proof-of-concept file (PoC.pkl) is available, increasing the risk of exploitation in the wild.

Detection Methods for CVE-2024-2057

Indicators of Compromise

• Unusual outbound requests from servers running LangChain applications to internal IP ranges (e.g., 169.254.169.254, 10.x.x.x, 192.168.x.x)
• Log entries showing the TFIDFRetriever load_local function being called with external URLs or unexpected file paths
• Anomalous network traffic patterns from application servers to cloud metadata endpoints
• Pickle file artifacts with suspicious serialized payloads in application directories

Detection Strategies

• Monitor network traffic for SSRF patterns including requests to cloud metadata services and internal network ranges
• Implement application logging to capture all inputs to the load_local function in TFIDFRetriever
• Deploy web application firewalls (WAF) with SSRF detection rules to block malicious request patterns
• Use SentinelOne's runtime protection to detect anomalous process behavior and unauthorized network connections

Monitoring Recommendations

• Enable verbose logging for LangChain applications, particularly around retriever operations
• Set up alerts for outbound connections to RFC 1918 private address ranges from public-facing applications
• Monitor for access attempts to sensitive internal endpoints such as cloud provider metadata services
• Regularly audit dependencies and ensure langchain_community is updated to version 0.0.27 or later

How to Mitigate CVE-2024-2057

Immediate Actions Required

• Upgrade langchain_community to version 0.0.27 or later immediately
• Audit all applications using TFIDFRetriever to identify potential exposure
• Implement network segmentation to limit the impact of potential SSRF attacks
• Review application logs for signs of exploitation attempts
• Block outbound requests to cloud metadata endpoints from application servers where not required

Patch Information

LangChain has addressed this vulnerability in version 0.0.27 of the langchain_community package. The fix was implemented via GitHub Pull Request #18695. Organizations should upgrade to the patched version as soon as possible. The vulnerability is tracked in VulDB as VDB-255372.

Workarounds

• Implement strict input validation on any user-supplied data before passing it to TFIDFRetriever functions
• Use network-level controls to restrict outbound connections from application servers to only necessary destinations
• Deploy an egress firewall to block requests to internal network ranges and cloud metadata endpoints
• Consider disabling the TFIDFRetriever component if not actively used until patching is complete

# Upgrade langchain_community to patched version
pip install --upgrade langchain_community>=0.0.27

# Verify installed version
pip show langchain_community | grep Version