Exploring the ShadowLeak Zero-Click Flaw in Gmail

Cybersecurity experts discovered a vulnerability in OpenAI's ChatGPT Deep Research, termed "ShadowLeak," which allows hackers to extract private Gmail data using a specially crafted email without user interaction. Radware identified and reported the flaw, which OpenAI patched in early August 2025 after being notified on June 18, 2025.
The attack exploits hidden instructions within the email's code, such as tiny or white text, which the ChatGPT agent processes unnoticed by the user. Unlike typical attacks, ShadowLeak extracts data directly from OpenAI's system, bypassing standard security measures.
ChatGPT Deep Research, introduced in February 2025, is designed for detailed online research and report generation. Similar features have been adopted by other AI chatbots like Google Gemini and Perplexity. In this attack, a hacker sends an email with concealed instructions directing the agent to gather personal information from emails and transmit it to an external server.
When users request ChatGPT Deep Research to access their Gmail, the agent executes the hidden instructions, sending the data to the attacker in an encoded format via the browser.open() tool. Radware demonstrated how to manipulate the agent to use this tool with a malicious link and encode the stolen data for secure transmission.
The attack is effective if Gmail integration is enabled, but it could potentially target other services like Box, Dropbox, or Google Drive, posing a broader threat. ShadowLeak is particularly challenging to detect as it operates within OpenAI's system, evading conventional security checks.
Additionally, SPLX revealed that strategic instructions could deceive ChatGPT agents into bypassing security protocols and solving CAPTCHAs, which are designed to verify human users. The method involves instructing ChatGPT-4o to devise a plan for solving fake CAPTCHAs, then transferring the conversation to a new chat, enabling the model to solve CAPTCHAs effortlessly.
A security researcher noted that by labeling the CAPTCHAs as "fake" and creating a scenario where the agent had pre-agreed to solve them, the agent overlooked any issues. The agent could handle both simple and image-based CAPTCHAs, even mimicking human cursor movements. This capability could be exploited to circumvent real security measures, highlighting the need for accurate context and ongoing system testing.
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