Executive Summary

CVE-2026-5532 is a critical Remote Code Execution (RCE) vulnerability in ScrapeGraphAI versions up to 1.74.0. It occurs in the function create_sandbox_and_execute of the GenerateCodeNode component, where Python code generated by a Large Language Model (LLM) is executed unsafely using Python's exec() function.

The vulnerability arises because the sandboxing is ineffective: the executed code has full access to Python's built-in functions, including dangerous ones like __import__(), open(), eval(), and exec(). An attacker can embed malicious prompt injection payloads inside HTML content on a webpage. When ScrapeGraphAI scrapes this attacker-controlled webpage, the malicious HTML influences the LLM to generate harmful Python code, which is then executed with full privileges.

This allows the attacker to execute arbitrary commands on the victim's system remotely, including running shell commands, reading or writing files, and exfiltrating data, leading to complete system compromise.

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Impact Analysis

This vulnerability can lead to a complete compromise of the affected system running ScrapeGraphAI. An attacker can remotely execute arbitrary code with the privileges of the ScrapeGraphAI process.

• Execution of arbitrary shell commands.
• Reading, modifying, or deleting files on the system.
• Exfiltration of sensitive data.
• Potential full system takeover depending on the environment and privileges.

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Detection Guidance

This vulnerability can be detected by monitoring for unexpected execution of Python code or suspicious network activity related to ScrapeGraphAI scraping attacker-controlled URLs.

Since the vulnerability involves execution of LLM-generated Python code with full builtins, detection can focus on identifying unusual Python exec() calls or unexpected subprocess executions.

Suggested commands to detect exploitation attempts include:

• Using system process monitoring tools (e.g., ps, top) to look for unexpected Python processes.
• Checking logs for network requests to suspicious or attacker-controlled URLs that ScrapeGraphAI might be scraping.
• Using command-line tools like `lsof` or `strace` on the ScrapeGraphAI process to detect unexpected file or network activity.
• Searching for suspicious Python code execution in logs or runtime traces, for example by grepping for exec() calls or subprocess usage.

Example command to check running Python processes: `ps aux | grep python`

Example command to monitor network connections: `netstat -tunap | grep scrapegraph-ai` or `ss -tunap | grep scrapegraph-ai`

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Mitigation Strategies

Immediate mitigation steps include:

• Stop using ScrapeGraphAI versions up to 1.74.0 until a patched version is available.
• Avoid scraping untrusted or attacker-controlled web pages that could contain malicious prompt injection payloads.
• If possible, disable or restrict the execution of LLM-generated code or sandbox it properly to prevent access to dangerous Python builtins.
• Monitor and restrict network access of ScrapeGraphAI to trusted sources only.
• Implement runtime monitoring to detect and block suspicious Python code execution or subprocess calls.

Since the vendor has not responded and no patch is currently available, these steps are critical to reduce risk of exploitation.

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Compliance Impact

This vulnerability allows remote attackers to execute arbitrary code on the victim's system, potentially leading to full system compromise.

Such a compromise could result in unauthorized access, modification, or exfiltration of sensitive data, which may violate data protection regulations such as GDPR and HIPAA.

Organizations using the affected ScrapeGraphAI versions could face compliance risks if the vulnerability is exploited, as it undermines the confidentiality, integrity, and availability of protected data.

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