ChatGPT Flaw Exposes User Files, Poses System Access Risk

Key Takeaways

• A critical vulnerability in ChatGPT allowed for local file inclusion (LFI) via a path traversal flaw, potentially exposing system files.
• The exploit chain leveraged a guardrail bypass to trick the LLM into generating a malicious download URL.
• Security researcher zer0dac discovered the flaw, which has since been remediated by OpenAI through a redesign of the URL download flow.
• While direct sensitive data disclosure was limited due to sandboxing, the vulnerability highlighted the potential for LFI as a building block in more complex AI exploit chains.

A significant security flaw within OpenAI’s ChatGPT platform recently came to light, involving a sophisticated attack chain that combined a guardrail bypass with a path traversal vulnerability. This intricate exploit could have enabled attackers to access restricted system files, such as /etc/passwd, by manipulating ChatGPT’s file download mechanism.

Security researcher zer0dac identified and reported the issue. OpenAI has since addressed the vulnerability by re-architecting the platform’s URL download process.

The Multi-Stage Exploitation Process

The successful exploitation of this vulnerability was not a single event but a four-step process culminating in local file inclusion (LFI). This method showcases the complex interplay between prompt engineering and traditional web application vulnerabilities in AI systems:

• Initial File Upload: The process began with the researcher uploading an innocuous HTML file to ChatGPT for analysis. This action established a legitimate, sandboxed file path within the system.
• Guardrail Evasion: Attempts to directly request a download link for the uploaded file were initially blocked by ChatGPT, which cited its standard policy of deleting temporary files. This refusal falls under OWASP’s LLM02:2025 (Sensitive Information Disclosure) category.
• LLM Manipulation: To circumvent this restriction, the researcher employed a social engineering technique. They first requested an edit to the previously uploaded file. Subsequently, they claimed the file had been “accidentally deleted” and asked for a re-download link. This deceptive prompt successfully tricked ChatGPT into generating a valid download URL, bypassing its internal deletion policy.
• Endpoint Analysis: The generated download link revealed a backend API structure: /backend-api/conversation/{id}/interpreter/download?message_id={id}&sandbox_path=/mnt/data/test.html.

Exploiting the File Download Flow

With a functional download endpoint at hand, the researcher focused on the sandbox_path parameter. A direct path traversal attempt, such as ../../../../etc/passwd, would likely have been thwarted by robust path validation mechanisms.

Instead, the researcher employed a more subtle approach. They retained the original, legitimate path (/mnt/data/test.html) and appended the traversal sequence afterward: /mnt/data/test.html/../../../../etc/passwd.

This method exploited inconsistencies in path normalization within the system. The validation logic was effectively tricked into perceiving the request as a legitimate file access within the sandbox, while the appended traversal sequence allowed the system to resolve the path outside the intended directory. When this specially crafted URL was accessed directly through a web browser, it successfully retrieved the contents of the /etc/passwd file from ChatGPT’s execution environment.

According to the researcher’s notes, the immediate practical impact of this specific disclosure was somewhat mitigated by ChatGPT’s sandboxed code execution environment. This meant that accessing a generic system file like /etc/passwd did not directly lead to the exposure of highly sensitive user data.

Nevertheless, this vulnerability underscores a critical lesson for AI security: even seemingly isolated LFI and path traversal primitives can serve as foundational elements in more elaborate exploit chains. This is particularly relevant in the context of agentic or tool-augmented LLM architectures, where sandboxes might have broader file system access or interact with other services.

OpenAI has since resolved the vulnerability by redesigning the URL download flow, though specific technical details regarding the implementation of the fix have not been publicly disclosed.

This incident highlights the convergence of two distinct but equally critical risk categories in LLM security: the manipulation of prompt-based guardrails (convincing an AI model to override its safety protocols through conversational framing) and traditional web application vulnerabilities (such as path traversal) manifesting within AI-generated backend endpoints.

As LLM platforms increasingly incorporate features like file uploads, code execution, and dynamic URL generation, cybersecurity experts emphasize the necessity of applying both AI-specific red teaming methodologies and conventional web application security testing in tandem to these evolving systems.

What You Should Do

• For Users: Always exercise caution when interacting with LLMs, especially regarding file uploads or requests for sensitive information. Be wary of unusual prompts or attempts to bypass standard safety features.
• For Developers/Organizations Using LLMs: Implement robust input validation and output sanitization for all user-provided data, particularly when handling file paths or generating URLs.
• Conduct Comprehensive Security Audits: Perform regular security audits that combine traditional web application penetration testing with AI-specific red teaming to identify vulnerabilities that arise from the unique interaction of LLM logic and backend infrastructure.
• Ensure Consistent Path Normalization: Developers should ensure that all components of their systems use consistent and secure path normalization techniques to prevent path traversal exploits.
• Stay Updated: Keep LLM platforms and underlying infrastructure updated to benefit from the latest security patches and design improvements.