CVE-2024-6971 — LOW (CVSS 3.4) AI Security Vulnerability

Q: Is CVE-2024-6971 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2024-6971, increasing the risk of exploitation.

Q: How to fix CVE-2024-6971?

1. Patch: Apply commit aeace796d861e922133b769710019608a6363264 or upgrade lollms past 9.5.1 once released. 2. Access control: Restrict lollms API endpoints to authenticated trusted users only—never expose the lollms server to untrusted networks or unauthenticated callers. 3. Filesystem isolation: Run lollms in a container or VM with a read-only bind mount limited to the intended data directory; block filesystem access outside that scope. 4. Detection: Monitor RAG endpoint requests for path traversal patterns (../, %2e%2e, etc.) and flag any requests referencing paths outside the designated lollms data directory. 5. Package audit: Review installed Python packages in the lollms environment for unexpected additions post-deployment.

Q: What systems are affected by CVE-2024-6971?

This vulnerability affects the following AI/ML architecture patterns: RAG pipelines, local AI assistants, agent frameworks, vector databases.

Q: What is the CVSS score for CVE-2024-6971?

CVE-2024-6971 has a CVSS v3.1 base score of 3.4 (LOW). The EPSS exploitation probability is 0.03%.

CISO Take

lollms ≤9.5.1 allows path traversal through RAG vectorization endpoints, letting high-privilege local attackers access arbitrary SQLite files and trigger unsanctioned package installations. Risk is constrained by local-only access and high-privilege requirement—not an internet-scale threat. Patch immediately if lollms runs in any shared or multi-user environment, and isolate the process filesystem.

Risk Assessment

CVSS 3.4 (Low) with local attack vector (AV:L) and high-privilege requirement (PR:H) significantly limits real-world exploitability. EPSS of 0.00027 confirms minimal active exploitation likelihood. Risk elevates meaningfully in shared development environments, CI/CD pipelines, or team-deployed lollms instances where multiple users share API access. No CISA KEV listing or confirmed active exploitation. The package installation side-effect is the most dangerous element, as it could escalate a low-severity traversal into code execution.

Affected Systems

Package	Ecosystem	Vulnerable Range	Patched
lollms	pip	<= 9.5.1	No patch
74 Pushed 2d ago 33% patched ~1d to patch Full package profile →

Do you use lollms? You're affected.

Severity & Risk

CVSS 3.1

3.4 / 10

EPSS

0.0%

chance of exploitation in 30 days

Higher than 8% of all CVEs

Source: EPSS v3 — FIRST.org

Exploitation Status

Exploit Available

Exploitation: MEDIUM

Sophistication

Trivial

Exploitation Confidence

medium

○ CISA SSVC: Public PoC

○ Public PoC indexed (trickest/cve)

Composite signal derived from CISA KEV, CISA SSVC, EPSS, trickest/cve, and Nuclei templates.

Attack Surface

AV Local

AC Low

PR High

UI None

S Unchanged

C None

I Low

A Low

Recommended Action

5 steps

Patch: Apply commit aeace796d861e922133b769710019608a6363264 or upgrade lollms past 9.5.1 once released.
Access control: Restrict lollms API endpoints to authenticated trusted users only—never expose the lollms server to untrusted networks or unauthenticated callers.
Filesystem isolation: Run lollms in a container or VM with a read-only bind mount limited to the intended data directory; block filesystem access outside that scope.
Detection: Monitor RAG endpoint requests for path traversal patterns (../, %2e%2e, etc.) and flag any requests referencing paths outside the designated lollms data directory.
Package audit: Review installed Python packages in the lollms environment for unexpected additions post-deployment.

CISA SSVC Assessment

Decision Track*

Exploitation poc

Automatable No

Technical Impact partial

Source: CISA Vulnrichment (SSVC v2.0). Decision based on the CISA Coordinator decision tree.

Classification

Data Extraction Code Execution RAG Framework Agent AML.T0037 - Data from Local System AML.T0049 - Exploit Public-Facing Application AML.T0064 - Gather RAG-Indexed Targets AML.T0085.000 - RAG Databases

Compliance Impact

This CVE is relevant to:

EU AI Act

Article 15 - Accuracy, robustness and cybersecurity

ISO 42001

A.9.4 - Information security for AI systems

NIST AI RMF

MANAGE-2.2 - Mechanisms to sustain and manage AI risks are implemented

OWASP LLM Top 10

LLM05 - Supply Chain Vulnerabilities LLM07 - Insecure Plugin Design

Frequently Asked Questions

What is CVE-2024-6971?

lollms ≤9.5.1 allows path traversal through RAG vectorization endpoints, letting high-privilege local attackers access arbitrary SQLite files and trigger unsanctioned package installations. Risk is constrained by local-only access and high-privilege requirement—not an internet-scale threat. Patch immediately if lollms runs in any shared or multi-user environment, and isolate the process filesystem.

Is CVE-2024-6971 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2024-6971, increasing the risk of exploitation.

How to fix CVE-2024-6971?

1. Patch: Apply commit aeace796d861e922133b769710019608a6363264 or upgrade lollms past 9.5.1 once released. 2. Access control: Restrict lollms API endpoints to authenticated trusted users only—never expose the lollms server to untrusted networks or unauthenticated callers. 3. Filesystem isolation: Run lollms in a container or VM with a read-only bind mount limited to the intended data directory; block filesystem access outside that scope. 4. Detection: Monitor RAG endpoint requests for path traversal patterns (../, %2e%2e, etc.) and flag any requests referencing paths outside the designated lollms data directory. 5. Package audit: Review installed Python packages in the lollms environment for unexpected additions post-deployment.

What systems are affected by CVE-2024-6971?

This vulnerability affects the following AI/ML architecture patterns: RAG pipelines, local AI assistants, agent frameworks, vector databases.

What is the CVSS score for CVE-2024-6971?

CVE-2024-6971 has a CVSS v3.1 base score of 3.4 (LOW). The EPSS exploitation probability is 0.03%.

Technical Details

NVD Description

A path traversal vulnerability exists in the ParisNeo/lollms repository, specifically in the `lollms_file_system.py` file. The functions `add_rag_database`, `toggle_mount_rag_database`, and `vectorize_folder` do not implement security measures such as `sanitize_path_from_endpoint` or `sanitize_path`. This allows an attacker to perform vectorize operations on `.sqlite` files in any directory on the victim's computer, potentially installing multiple packages and causing a crash.

Exploitation Scenario

A developer or internal user with high-privilege access to a shared lollms deployment crafts a request to the vectorize_folder endpoint using path traversal sequences (e.g., ../../../home/user/.config/browser/Default/Login Data) pointing to a directory containing sensitive SQLite databases such as browser credential stores or application databases. The lollms vectorization pipeline ingests and embeds the file contents into the RAG vector store without restriction. The attacker can then query the lollms assistant naturally to retrieve indexed credential data. As a secondary effect, the vectorization process installs unexpected Python packages, enabling persistence or further lateral movement within the development environment.