CVE-2025-1497: Mljar Plotai RCE Vulnerability

CVE-2025-1497 Overview

A critical Remote Code Execution (RCE) vulnerability has been identified in PlotAI, an AI-powered data visualization library developed by mljar. The vulnerability stems from a lack of validation of Large Language Model (LLM)-generated output, allowing attackers to execute arbitrary Python code on systems running the affected software.

The vendor has acknowledged the vulnerability but has chosen not to release a formal security patch. Instead, the vulnerable code line has been commented out, meaning users who wish to continue using the full functionality must uncomment the line and explicitly accept the associated security risks.

Critical Impact

This vulnerability allows unauthenticated remote attackers to execute arbitrary Python code through maliciously crafted LLM-generated output, potentially leading to complete system compromise. The vendor will not be releasing a formal patch.

Affected Products

• mljar PlotAI (all versions)
• Python environments with PlotAI installed and exec() enabled
• Applications integrating PlotAI for AI-driven data visualization

Discovery Timeline

• 2025-03-10 - CVE-2025-1497 published to NVD
• 2025-10-03 - Last updated in NVD database

Technical Details for CVE-2025-1497

Vulnerability Analysis

This vulnerability represents a Code Injection flaw (CWE-94) combined with Command Injection characteristics (CWE-77). The core issue lies in how PlotAI processes output generated by Large Language Models without proper validation or sanitization before execution.

When PlotAI receives code generated by an LLM for data visualization purposes, it directly passes this code to Python's built-in exec() function. This creates a direct code execution path where any arbitrary Python code embedded in or generated as LLM output will be executed with the same privileges as the PlotAI process. The attack surface is network-accessible, requires no privileges or user interaction, and can result in complete compromise of confidentiality, integrity, and availability.

Root Cause

The root cause is the unsafe use of Python's exec() function to execute LLM-generated code without any validation, sandboxing, or sanitization. The exec() function is inherently dangerous as it can execute any valid Python code, including system commands, file operations, and network connections.

The vulnerable code path in plotai/code/executor.py directly passes LLM-generated strings to exec(), trusting that the LLM output is benign. This trust model is fundamentally flawed because:

1. LLM outputs can be manipulated through prompt injection attacks
2. LLMs may generate unintended code due to ambiguous or malicious input
3. There is no allowlist or denylist filtering of dangerous operations

Attack Vector

The attack vector is network-based and requires no authentication or user interaction. An attacker can exploit this vulnerability by:

1. Providing malicious input that influences the LLM to generate harmful Python code
2. Leveraging prompt injection techniques to bypass any content filters
3. Crafting inputs that result in system command execution, file exfiltration, or reverse shell establishment

The following patch shows the vendor's mitigation approach - commenting out the dangerous exec() call:

                if not line.startswith("```"):
                    tmp_code += line + "\n"

            # please be aware of security issue with exec functions
            # LLM can execute arbitrary code
            # if you are aware of security issues, please uncomment below line

            # exec(tmp_code, globals_env, locals_env)
            
        except Exception as e:
            return str(e)
        return None

Source: GitHub PlotAI Commit Details

Detection Methods for CVE-2025-1497

Indicators of Compromise

• Unexpected Python process spawning child processes or making network connections
• Unusual file system activity originating from PlotAI execution contexts
• Evidence of reverse shell connections or command-and-control traffic
• Log entries showing execution of system commands through Python's os or subprocess modules

Detection Strategies

• Monitor Python process trees for anomalous child process creation, especially shells or network utilities
• Implement application-level logging to capture all code passed to exec() functions
• Deploy endpoint detection rules for common Python-based exploitation techniques
• Use behavioral analysis to identify deviations from normal PlotAI operational patterns

Monitoring Recommendations

• Enable verbose logging for PlotAI and any applications integrating it
• Monitor outbound network connections from systems running PlotAI
• Implement file integrity monitoring on systems with PlotAI deployed
• Alert on any attempts to import dangerous Python modules like os, subprocess, or socket within PlotAI contexts

How to Mitigate CVE-2025-1497

Immediate Actions Required

• Audit your environment to identify all instances of PlotAI deployment
• Ensure the vulnerable exec() line remains commented out in production environments
• Implement network segmentation to limit the blast radius of potential exploitation
• Consider removing PlotAI from production systems until a secure alternative is available

Patch Information

The vendor has explicitly stated they will not release a security patch. Instead, the mitigation implemented by mljar involves commenting out the vulnerable exec() line in plotai/code/executor.py. Users who require the full functionality must manually uncomment this line, thereby accepting the inherent security risks.

Review the vendor's commit for details: GitHub PlotAI Commit Details

Additional resources:

• CERT.PL CVE-2025-1497 Analysis
• CERT.PL CVE-2025-1497 Advisory

Workarounds

• Do not uncomment the exec() line in production environments
• Implement strict input validation and sanitization before any data reaches PlotAI
• Consider running PlotAI in an isolated container or sandbox environment with limited privileges
• Evaluate alternative data visualization libraries that do not execute LLM-generated code

# Verify the exec() line is commented in your PlotAI installation
grep -n "exec(tmp_code" /path/to/plotai/code/executor.py
# Should show the line is commented: "# exec(tmp_code, globals_env, locals_env)"