แนวคิดหลัก
Large Language Models (LLMs) can be tricked into bypassing their safety mechanisms and generating harmful content through a novel attack method called Feign Agent Attack (F2A), which exploits LLMs' blind trust in security detection agents.
บทคัดย่อ
This research paper introduces a new security vulnerability, termed Feign Agent Attack (F2A), targeting Large Language Models (LLMs). The core concept revolves around exploiting the inherent trust LLMs place in integrated security detection agents.
The authors detail the three-step methodology of F2A:
- Convert Malicious Content: Disguise malicious text as benign Python code strings to circumvent initial security filters.
- Feign Security Detection Results: Embed fabricated security assessments within the code, misrepresenting the content as safe.
- Construct Task Instructions: Craft a series of instructions that lead the LLM to execute the disguised malicious code based on the fabricated safety confirmation.
Experiments conducted on various LLMs, including Mistral Large-2, Deepseek-V2.5, and GPT-4o, demonstrate a high success rate of F2A across diverse malicious prompts. The research highlights the vulnerability of LLMs to seemingly legitimate instructions, particularly in areas like fraud, antisocial behavior, and politically sensitive topics, where the line between benign and harmful content can be blurred.
The paper also proposes a defense mechanism against F2A. By prompting LLMs to critically evaluate the source and validity of security detection results, the attack's effectiveness is significantly reduced. This highlights the importance of incorporating robust verification mechanisms within LLMs to mitigate the risks of blind trust in external agents.
The research concludes by emphasizing the need for continuous improvement in LLM security measures, particularly in light of evolving attack strategies like F2A. Future research directions include refining defense mechanisms, exploring the impact of F2A on different LLM architectures, and developing comprehensive security frameworks to address the evolving landscape of LLM vulnerabilities.
สถิติ
10 different types of dangerous prompts were used in the experiment, covering areas like death, weapon manufacturing, racial discrimination, poison, fraud, tutorials on illegal activities, antisocial behavior, tendencies towards mental illness, politically sensitive topics, and terrorist activities.
The F2A attack was successful on mainstream LLMs and their corresponding services available on the web.
Prompts related to fraud, antisocial behavior, tendencies towards mental illness, and politically sensitive topics were found to be more difficult for the models to detect and defend against.
The Defe-Prompt defense mechanism was able to detect the vast majority of attacks in a timely manner.
คำพูด
"By fabricating security detection results within chat content, LLMs can be easily compromised."
"This malicious method bypasses the model’s defense for chat content, preventing the triggering of the refusal mechanism."
"The results indicate that most LLM services exhibit blind trust in security detection agents, leading to the non-triggering of rejection mechanisms."