CVE-2024-41115: Streamlit eval() injection

CISO Take

Any internet-exposed instance of streamlit-geospatial is fully compromised by an unauthenticated attacker with a single HTTP request. The palette parameter is passed raw to Python's eval(), granting arbitrary code execution as the server process. Patch immediately to commit c4f81d9616d or restrict network access — there is no authentication barrier protecting this.

What is the risk?

Extremely high. CVSS 9.8 with AV:N/AC:L/PR:N/UI:N means this is trivially exploitable by anyone with network access, no credentials or user interaction required. Streamlit apps are routinely deployed on internal data science platforms, Jupyter-adjacent infrastructure, and occasionally exposed publicly for demos or collaboration. The eval()-on-user-input pattern is one of the oldest and most well-understood RCE classes — exploit code requires no AI/ML knowledge whatsoever.

What systems are affected?

Package	Ecosystem	Vulnerable Range	Patched
Streamlit	pip	—	No patch
45.0K OpenSSF 7.2 2.9K dependents Pushed 3d ago 7% patched ~0d to patch Full package profile →

Do you use Streamlit? You're affected.

How severe is it?

CVSS 3.1

9.8 / 10

EPSS

1.5%

chance of exploitation in 30 days

Higher than 70% 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 High

A High

What should I do?

5 steps

PATCH

Update to commit c4f81d9616d40c60584e36abb15300853a66e489 immediately.
WORKAROUND (if patching is delayed): Replace eval() with a whitelist-based palette selector that validates input against a predefined list of allowed colormap names — never pass user input to eval(), exec(), or subprocess with shell=True.
NETWORK

If the app is not required to be public, restrict access via firewall rules or VPN.
DETECTION

Review web server logs for palette parameter values containing Python builtins (__import__, os, subprocess, open) or non-colormap strings; monitor for unexpected child processes spawned by the Streamlit Python process.
AUDIT

Inventory all Streamlit-based internal tools for similar eval()/exec() patterns — this vulnerability class is frequently replicated across data science apps.

What does CISA's SSVC say?

Decision Attend

Exploitation poc

Automatable Yes

Technical Impact total

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

How is it classified?

Code Execution Data Extraction Framework AML.T0035 - AI Artifact Collection AML.T0037 - Data from Local System AML.T0049 - Exploit Public-Facing Application AML.T0050 - Command and Scripting Interpreter AML.T0072 - Reverse Shell

Which compliance frameworks are affected?

This CVE is relevant to:

EU AI Act

Article 15 - Accuracy, Robustness and Cybersecurity

ISO 42001

6.1.2 - AI Risk Assessment A.6.2.6 - AI System Technical Security Controls

NIST AI RMF

MANAGE-2.2 - Mechanisms for Responding to AI Risks MAP-5.1 - Likelihood and Impact Estimation

OWASP LLM Top 10

LLM06:2025 - Excessive Agency

Frequently Asked Questions

What is CVE-2024-41115?

Any internet-exposed instance of streamlit-geospatial is fully compromised by an unauthenticated attacker with a single HTTP request. The palette parameter is passed raw to Python's eval(), granting arbitrary code execution as the server process. Patch immediately to commit c4f81d9616d or restrict network access — there is no authentication barrier protecting this.

Is CVE-2024-41115 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2024-41115, increasing the risk of exploitation.

How to fix CVE-2024-41115?

1. PATCH: Update to commit c4f81d9616d40c60584e36abb15300853a66e489 immediately. 2. WORKAROUND (if patching is delayed): Replace eval() with a whitelist-based palette selector that validates input against a predefined list of allowed colormap names — never pass user input to eval(), exec(), or subprocess with shell=True. 3. NETWORK: If the app is not required to be public, restrict access via firewall rules or VPN. 4. DETECTION: Review web server logs for palette parameter values containing Python builtins (__import__, os, subprocess, open) or non-colormap strings; monitor for unexpected child processes spawned by the Streamlit Python process. 5. AUDIT: Inventory all Streamlit-based internal tools for similar eval()/exec() patterns — this vulnerability class is frequently replicated across data science apps.

What systems are affected by CVE-2024-41115?

This vulnerability affects the following AI/ML architecture patterns: ML/data science web applications, Geospatial ML pipelines, Internal data science platforms, Research and demo environments, MLOps tooling (Streamlit-based).

What is the CVSS score for CVE-2024-41115?

CVE-2024-41115 has a CVSS v3.1 base score of 9.8 (CRITICAL). The EPSS exploitation probability is 1.47%.

What is the AI security impact?

Affected AI Architectures

ML/data science web applicationsGeospatial ML pipelinesInternal data science platformsResearch and demo environmentsMLOps tooling (Streamlit-based)

MITRE ATLAS Techniques

AML.T0035 AI Artifact Collection

AML.T0037 Data from Local System

AML.T0049 Exploit Public-Facing Application

AML.T0050 Command and Scripting Interpreter

AML.T0072 Reverse Shell

Compliance Controls Affected

EU AI Act: Article 15

ISO 42001: 6.1.2, A.6.2.6

NIST AI RMF: MANAGE-2.2, MAP-5.1

OWASP LLM Top 10: LLM06:2025

What are the technical details?

Original Advisory

streamlit-geospatial is a streamlit multipage app for geospatial applications. Prior to commit c4f81d9616d40c60584e36abb15300853a66e489, the `palette` variable on line 488 in `pages/1_📷_Timelapse.py` takes user input, which is later used in the `eval()` function on line 493, leading to remote code execution. Commit c4f81d9616d40c60584e36abb15300853a66e489 fixes this issue.

Exploitation Scenario

An adversary scans for Streamlit applications using tool fingerprinting or Shodan queries for Streamlit's default port (8501) or deployment signatures. They navigate to the Timelapse page of streamlit-geospatial and submit a crafted palette value such as: __import__('subprocess').check_output(['id']) or a full reverse shell payload. The server's eval() executes this Python expression server-side with no validation. The attacker receives a shell with the privileges of the Streamlit process, then proceeds to dump environment variables for cloud credentials, exfiltrate model artifacts and training datasets from the filesystem, and establish persistence or pivot to adjacent internal services. The entire attack chain requires no authentication and no prior knowledge of the target environment.

Weaknesses (CWE)

CWE-20 Improper Input Validation

CWE-20 — Improper Input Validation: The product receives input or data, but it does not validate or incorrectly validates that the input has the properties that are required to process the data safely and correctly.

[Architecture and Design] Consider using language-theoretic security (LangSec) techniques that characterize inputs using a formal language and build "recognizers" for that language. This effectively requires parsing to be a distinct layer that effectively enforces a boundary between raw input and internal data representations, instead of allowing parser code to be scattered throughout the program, where it could be subject to errors or inconsistencies that create weaknesses. [REF-1109] [REF-1110] [REF-1111]
[Architecture and Design] Use an input validation framework such as Struts or the OWASP ESAPI Validation API. Note that using a framework does not automatically address all input validation problems; be mindful of weaknesses that could arise from misusing the framework itself (CWE-1173).

Source: MITRE CWE corpus.