Source URL: https://www.schneier.com/blog/archives/2024/11/race-condition-attacks-against-llms.html
Source: Schneier on Security
Title: Race Condition Attacks against LLMs
Feedly Summary: These are two attacks against the system components surrounding LLMs:
We propose that LLM Flowbreaking, following jailbreaking and prompt injection, joins as the third on the growing list of LLM attack types. Flowbreaking is less about whether prompt or response guardrails can be bypassed, and more about whether user inputs and generated model outputs can adversely affect these other components in the broader implemented system.
[…]
When confronted with a sensitive topic, Microsoft 365 Copilot and ChatGPT answer questions that their first-line guardrails are supposed to stop. After a few lines of text they halt—seemingly having “second thoughts”—before retracting the original answer (also known as Clawback), and replacing it with a new one without the offensive content, or a simple error message. We call this attack “Second Thoughts.”…
AI Summary and Description: Yes
Summary: The text discusses emerging attack types against Large Language Models (LLMs), specifically focusing on “Flowbreaking” and “Second Thoughts.” These attack vectors exploit the surrounding application architecture and guardrails rather than the models themselves, highlighting significant vulnerabilities that may threaten information security within AI-powered systems.
Detailed Description: This content is highly relevant for professionals in AI security, specifically in understanding the new vulnerabilities targeting the architecture and components of LLMs.
– **Flowbreaking Attack**:
– This is categorized as a new threat, joining the ranks of existing attacks such as jailbreaking and prompt injection.
– The key concern is whether user inputs and model outputs can compromise other system components, which are integral to maintaining security and functionality.
– **Second Thoughts Attack**:
– This occurs when AI models like Microsoft 365 Copilot or ChatGPT provide answers to sensitive queries despite their first-layer guardrails.
– The model may retract or change an answer after momentarily providing it, known as “Clawback.”
– This indicates a failure in the guardrail system, leading to potential leakage of sensitive information before a retraction occurs.
– **Stop Button Vulnerability**:
– Users can trigger a failure in the second-layer guardrails by stopping the answer generation. This allows the user to receive outputs that likely violate system policies.
– This showcases a flaw not in the AI model but in its application architecture, emphasizing how unintended interactions can lead to the exploitation of guardrails.
– **Implications for Security**:
– There are significant vulnerabilities in the code that bridges user input to LLM processing and output.
– As LLM systems become more complex, the interdependencies of various system components create new opportunities for attack.
– This analysis indicates a pressing need for improved security measures focusing on application architecture, not just the AI algorithms themselves.
In conclusion, the insights from this text reveal critical considerations for the security and compliance landscape, particularly regarding the integrity of AI systems. Continuous monitoring and a robust security framework are suggested to protect against emerging threats that may arise from the increasing adoption of LLMs in various applications.