ChatGPT Operator Prompt Injection Exploit Leaks Private Data

According to recent findings by cybersecurity researcher Johann Rehberger, OpenAI’s ChatGPT Operator, an experimental agent designed to automate web-based tasks, faces critical security risks from prompt injection attacks that could expose users’ private data.

In a demonstration shared exclusively with OpenAI last month, Rehberger showcased how malicious actors could hijack the AI agent to extract sensitive personal information—including email addresses, phone numbers, and physical addresses—from authenticated accounts. 

The exploits highlight vulnerabilities in AI-driven automation tools, even as companies like OpenAI implement multi-layered safeguards to reduce risks.

Exploit Methodology and Demonstration

Rehberger’s attack exploited ChatGPT Operator’s tendency to follow hyperlinks and interact with text fields without rigorous scrutiny.

He tricked the AI into navigating to a third-party webpage designed to capture keystrokes in real-time by hosting a prompt injection payload on a GitHub issue. 

This “sneaky data leakage” tool transmitted typed information directly to an attacker-controlled server without requiring form submissions or button clicks, bypassing OpenAI’s confirmation protocols.

In one test, the agent accessed a Y Combinator Hacker News account’s private settings page, copied the user’s admin-only email address, and pasted it into the malicious text field—sending the data to Rehberger’s server. 

A similar attack targeted Booking.com, leaking a user’s home address and contact details. While these demonstrations relied on authenticated sessions, they underscore the risks of AI agents interacting with sensitive websites while logged in.

OpenAI’s Mitigation Strategies

OpenAI’s current defenses include user monitoring prompts, which alert users to scrutinize the agent’s actions; inline confirmation requests before critical steps like button clicks; and out-of-band confirmations for cross-website operations. 

However, Rehberger noted inconsistencies in these safeguards. During initial tests, the agent sometimes executed actions like updating a user’s status without seeking approval.

The company also employs backend “prompt injection monitoring” systems that analyze HTTP traffic for suspicious patterns1. Yet, these probabilistic defenses often trigger only at the final stage of an attack, focusing on blocking harmful actions rather than preventing the initial exploitation. 

As Rehberger observed, “Mitigations reduce but don’t eliminate risks. Agents could become akin to malicious insiders in corporate environments”.

This research amplifies longstanding concerns about prompt injection vulnerabilities, which remain a “party pooper” for autonomous AI systems. 

While tools like ChatGPT Operator promise efficiency in tasks like travel booking or grocery shopping, their inability to reliably distinguish adversarial instructions poses existential challenges.

Rehberger argues that until robust solutions emerge, human-AI collaboration will require continuous oversight.

OpenAI has not disclosed specific countermeasures following Rehberger’s disclosure but emphasizes ongoing improvements to its threat detection models.

Independent experts urge platforms to consider embedding AI-specific identifiers—such as unique user-agent strings—to help websites block unauthorized agent access. 

For now, users are advised to restrict ChatGPT Operator’s access to sensitive accounts and monitor its activity closely during high-risk operations.

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