December 9, 2024 at 07:07AM
A patched security flaw in DeepSeek AI allows prompt injection attacks, enabling account takeover via cross-site scripting (XSS). Researcher Johann Rehberger demonstrated this vulnerability, revealing similar risks in other AI tools. Techniques like ZombAIs and Terminal DiLLMa exploit these weaknesses, raising concerns about security in generative AI applications.
### Meeting Takeaways – December 9, 2024
1. **Security Flaw in DeepSeek AI Chatbot**:
– A patched security vulnerability was identified in the DeepSeek chatbot, allowing potential account takeovers via prompt injection attacks.
– Security researcher Johann Rehberger reported that specific inputs could trigger JavaScript execution, leading to cross-site scripting (XSS) vulnerabilities.
2. **Mechanism of Attack**:
– The prompt injection technique allows attackers to access a user’s session token stored in localStorage on the chat domain, facilitating account impersonation.
– Attackers could exploit crafted prompts to extract sensitive information.
3. **Related Vulnerabilities**:
– Rehberger demonstrated risks in Anthropic’s Claude Computer Use, where prompt injection could enable remote command execution through AI controls.
– New attack vectors termed “ZombAIs” utilize this method to execute malicious commands and take control of the system.
4. **Command-Line Interface Threats**:
– Termed “Terminal DiLLMa,” a new attack leverages LLM outputs to hijack system terminals via prompt injection, targeting LLM-integrated CLI tools.
5. **Risks with Large Language Models**:
– Researchers from the University of Wisconsin-Madison and Washington University in St. Louis uncovered that OpenAI’s ChatGPT can be manipulated into rendering unsafe external content, bypassing constraints for malicious purposes.
– Prompt injections could also indirectly invoke ChatGPT plugins without user verification, posing additional privacy risks.
6. **Recommendations**:
– Developers and application designers should be aware of the untrusted nature of LLM outputs and the potential for arbitrary data execution.
– It is crucial to reconsider the context in which LLM outputs are utilized to mitigate risks relating to security vulnerabilities.
### Follow-Up Actions:
– Monitor updates and patches from DeepSeek and relevant AI platforms.
– Increase awareness within the development team about the risks of prompt injection and safe handling of LLM outputs.