CVE-2024-4941: Gradio: LFI via JSON path key exposes server files

HIGH PoC AVAILABLE CISA: TRACK*

Published June 6, 2024

CISO Take

Any internet-exposed Gradio instance ≤4.25 allows unauthenticated attackers to read arbitrary server files — including model weights, API keys, and .env configs — with a single crafted request. Patch to v4.26+ immediately and rotate all secrets on affected hosts. If Gradio fronted sensitive AI assets and was publicly reachable, treat as a confirmed compromise.

What is the risk?

CVSS 7.5 understates real-world impact. Gradio is pervasively deployed for ML demos, internal model UIs, and production inference frontends — frequently co-located with .env files holding OpenAI/HuggingFace/Anthropic API keys and model checkpoints. Zero authentication required, exploit is trivial, and a working PoC is publicly documented on HackerOne. The combination of broad Gradio adoption and high-value adjacent assets elevates this to critical operational risk for AI/ML teams.

What systems are affected?

Package	Ecosystem	Vulnerable Range	Patched
Gradio	pip	—	No patch
43.0K OpenSSF 5.6 685 dependents Pushed 4d ago 26% patched ~110d to patch Full package profile →

Do you use Gradio? You're affected.

How severe is it?

CVSS 3.1

7.5 / 10

EPSS

0.8%

chance of exploitation in 30 days

Higher than 53% 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 Network

AC Low

PR None

UI None

S Unchanged

C High

I None

A None

What should I do?

5 steps

Patch: Upgrade to Gradio ≥4.26 immediately (fix commit: ee1e294).
Workaround: If patching is blocked, disable the JSON component or restrict the Gradio instance to internal networks via firewall.
Rotate credentials: Audit and rotate all API keys, tokens, and secrets on affected hosts — assume .env and config files were read if exposure window existed.
Detect exploitation: Search web server logs for requests matching /file=.*\.env, /file=.*/etc/, /file=.*id_rsa — unexpected paths outside the Gradio upload directory are indicators of exploitation.
Asset inventory: Enumerate all Gradio instances in your environment; prioritize internet-facing and those with filesystem access to sensitive AI assets.

What does CISA's SSVC say?

Decision Track*

Exploitation poc

Automatable Yes

Technical Impact partial

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

How is it classified?

Data Extraction Data Leakage Framework Inference AML.T0025 - Exfiltration via Cyber Means AML.T0035 - AI Artifact Collection AML.T0037 - Data from Local System AML.T0049 - Exploit Public-Facing Application

Which compliance frameworks are affected?

This CVE is relevant to:

EU AI Act

Article 15 - Accuracy, robustness and cybersecurity

ISO 42001

A.8.2 - AI system data security

NIST AI RMF

MANAGE-2.2 - Mechanisms for identifying and implementing risk treatments for AI risks

OWASP LLM Top 10

LLM06:2025 - Excessive Agency / Sensitive Information Disclosure

Frequently Asked Questions

What is CVE-2024-4941?

Is CVE-2024-4941 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2024-4941, increasing the risk of exploitation.

How to fix CVE-2024-4941?

1. Patch: Upgrade to Gradio ≥4.26 immediately (fix commit: ee1e294). 2. Workaround: If patching is blocked, disable the JSON component or restrict the Gradio instance to internal networks via firewall. 3. Rotate credentials: Audit and rotate all API keys, tokens, and secrets on affected hosts — assume .env and config files were read if exposure window existed. 4. Detect exploitation: Search web server logs for requests matching /file=.*\.env, /file=.*/etc/, /file=.*id_rsa — unexpected paths outside the Gradio upload directory are indicators of exploitation. 5. Asset inventory: Enumerate all Gradio instances in your environment; prioritize internet-facing and those with filesystem access to sensitive AI assets.

What systems are affected by CVE-2024-4941?

This vulnerability affects the following AI/ML architecture patterns: model serving, ML demos and prototypes, inference endpoints.

What is the CVSS score for CVE-2024-4941?

CVE-2024-4941 has a CVSS v3.1 base score of 7.5 (HIGH). The EPSS exploitation probability is 0.83%.

What is the AI security impact?

Affected AI Architectures

model servingML demos and prototypesinference endpoints

MITRE ATLAS Techniques

AML.T0025 Exfiltration via Cyber Means

AML.T0035 AI Artifact Collection

AML.T0037 Data from Local System

AML.T0049 Exploit Public-Facing Application

Compliance Controls Affected

EU AI Act: Article 15

ISO 42001: A.8.2

NIST AI RMF: MANAGE-2.2

OWASP LLM Top 10: LLM06:2025

What are the technical details?

Original Advisory

A local file inclusion vulnerability exists in the JSON component of gradio-app/gradio version 4.25. The vulnerability arises from improper input validation in the `postprocess()` function within `gradio/components/json_component.py`, where a user-controlled string is parsed as JSON. If the parsed JSON object contains a `path` key, the specified file is moved to a temporary directory, making it possible to retrieve it later via the `/file=..` endpoint. This issue is due to the `processing_utils.move_files_to_cache()` function traversing any object passed to it, looking for a dictionary with a `path` key, and then copying the specified file to a temporary directory. The vulnerability can be exploited by an attacker to read files on the remote system, posing a significant security risk.

Exploitation Scenario

An attacker identifies an organization's public Gradio ML demo via Shodan or direct enumeration. They POST a crafted JSON payload to the JSON component endpoint containing {"path": "/app/.env"}, triggering move_files_to_cache() to copy the .env file to /tmp/gradio/[uuid]/.env. A subsequent GET to /file=/tmp/gradio/[uuid]/.env returns the file contents — exposing OPENAI_API_KEY, HUGGINGFACE_TOKEN, or database credentials. The attacker uses the extracted API keys to query production LLM APIs at the victim's cost, pivot to HuggingFace to exfiltrate private model weights, or use database credentials to access training data stores. No authentication, no special tooling — a single crafted HTTP request pairs with a follow-up fetch.

Weaknesses (CWE)

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

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.