CVE-2024-6971: LoLLMs path traversal — LOW

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 0.32%.

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.

What is the risk?

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.

What systems are affected?

Package	Ecosystem	Vulnerable Range	Patched
LoLLMs	pip	<= 9.5.1	No patch
81 Pushed 8d ago 33% patched ~1d to patch Full package profile →

Do you use LoLLMs? You're affected.

How severe is it?

CVSS 3.1

3.4 / 10

EPSS

0.3%

chance of exploitation in 30 days

Higher than 23% 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, VulnCheck KEV, CISA SSVC, EPSS, Metasploit, Exploit-DB, trickest/cve, Nuclei templates, and inthewild.io exploitation reports.

What is the attack surface?

AV Local

AC Low

PR High

UI None

S Unchanged

C None

I Low

A Low

What should I do?

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.

What does CISA's SSVC say?

Decision Track*

Exploitation poc

Automatable No

Technical Impact partial

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

How is it classified?

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

Which compliance frameworks are affected?

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 0.32%.

What is the AI security impact?

Affected AI Architectures

RAG pipelineslocal AI assistantsagent frameworksvector databases

MITRE ATLAS Techniques

AML.T0037 Data from Local System

AML.T0049 Exploit Public-Facing Application

AML.T0064 Gather RAG-Indexed Targets

AML.T0085.000 RAG Databases

Compliance Controls Affected

EU AI Act: Article 15

ISO 42001: A.9.4

NIST AI RMF: MANAGE-2.2

OWASP LLM Top 10: LLM05, LLM07

What are the technical details?

Original Advisory

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.

Weaknesses (CWE)

CWE-22 Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal') Primary

CWE-22 — Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal'): The product uses external input to construct a pathname that is intended to identify a file or directory that is located underneath a restricted parent directory, but the product does not properly neutralize special elements within the pathname that can cause the pathname to resolve to a location that is outside of the restricted directory.

[Implementation] Assume all input is malicious. Use an "accept known good" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does. When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, "boat" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as "red" or "blue." Do not rely exclusively on looking for malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if the code's environment changes. This can give attackers enough room to bypass the intended validation. However, denylis
[Architecture and Design] For any security checks that are performed on the client side, ensure that these checks are duplicated on the server side, in order to avoid CWE-602. Attackers can bypass the client-side checks by modifying values after the checks have been performed, or by changing the client to remove the client-side checks entirely. Then, these modified values would be submitted to the server.

Source: MITRE CWE corpus.