Exploiting Retrieval Augmented Generation in LLM-Powered Applications to Craft Imperceptible Malicious Responses

Conceptos Básicos

Attackers can craft visually indistinguishable malicious documents that, when used as reference sources for Retrieval Augmented Generation (RAG) in LLM-powered applications, can mislead the applications into generating incorrect and potentially harmful responses.

Resumen

The paper introduces a new threat to LLM-powered applications, termed "retrieval poisoning", where attackers can exploit the design features of LLM application frameworks to craft malicious documents that appear identical to benign ones in human perception.
The key insights are:

Attackers can analyze the critical components of LLM application frameworks used for RAG, such as document parsers, text splitters, and prompt templates, to identify ways to invisibly inject attack sequences into external documents.

Attackers use a gradient-guided token mutation technique to generate attack sequences that can manipulate the LLMs into producing targeted malicious responses, while maintaining the appearance of the original document.

Preliminary experiments show that retrieval poisoning can achieve an 88.33% average attack success rate on three different open-source LLMs, and a 66.67% success rate in a real-world LLM-powered application developed with LangChain.

The paper highlights the need for more effective mitigation strategies to address the security risks posed by retrieval poisoning, as current LLM-powered applications are highly vulnerable to such attacks.

Estadísticas

The average length of the generated attack sequences is 30.36 tokens.
The average length of the augmented requests is 595.29 tokens.
The average length of the output responses is 135.93 tokens.

Citas

"Retrieval poisoning fully leverages the RAG workflow, exhibiting a significant threat to the LLM ecosystem."
"Retrieval poisoning poses an extreme danger to current applications, necessitating the urgent development of more effective mitigation strategies."

Ideas clave extraídas de

Human-Imperceptible Retrieval Poisoning Attacks in LLM-Powered Applications

by Quan Zhang,B... a las arxiv.org 04-29-2024

https://arxiv.org/pdf/2404.17196.pdf

Consultas más profundas

How can LLM application frameworks be designed to better detect and mitigate retrieval poisoning attacks?

To enhance the resilience of LLM application frameworks against retrieval poisoning attacks, several design considerations can be implemented:

Input Sanitization: Implement robust input validation mechanisms to detect and filter out potentially malicious content embedded in external documents. This can involve thorough content parsing and validation to identify any hidden attack sequences.

Content Verification: Introduce mechanisms for verifying the authenticity and integrity of external content used for retrieval. This can include checksum verification, digital signatures, or content hashing to ensure the content has not been tampered with.

Anomaly Detection: Incorporate anomaly detection algorithms to identify unusual patterns or discrepancies in the retrieved content that may indicate the presence of malicious attack sequences.

Prompt Template Validation: Validate prompt templates to ensure that the augmented requests generated from external content align with the expected structure and content. Any deviations could signal a potential attack.

Response Verification: Implement response verification mechanisms to cross-reference the generated responses with the original content to detect any discrepancies or malicious alterations.

Regular Updates and Security Patches: Keep the framework updated with the latest security patches and enhancements to address any vulnerabilities that could be exploited by attackers.

User Awareness: Educate users about the risks associated with retrieval poisoning attacks and encourage them to verify the source of external content before using it in LLM-powered applications.

By incorporating these design strategies, LLM application frameworks can better detect and mitigate retrieval poisoning attacks, enhancing the overall security of the applications.

What other types of attacks could be developed that exploit the integration of external content in LLM-powered applications?

Apart from retrieval poisoning attacks, several other types of attacks could exploit the integration of external content in LLM-powered applications:

Data Poisoning Attacks: Attackers could inject malicious data into the external content used for training LLMs, leading to biased or inaccurate responses generated by the models.

Adversarial Examples: Adversarial examples could be crafted in the external content to deceive LLMs into generating incorrect responses or performing unintended actions.

Model Inversion Attacks: By analyzing the responses generated by LLMs, attackers could infer sensitive information about the training data or manipulate the model's internal representations.

Privacy Violations: External content containing personally identifiable information or sensitive data could be exploited to extract confidential details or compromise user privacy.

Model Extraction Attacks: Attackers could reverse-engineer the LLM model by leveraging external content to extract its parameters, architecture, or training data, leading to intellectual property theft.

Sybil Attacks: Creating fake external content sources or personas to influence the LLM's training or inference process, leading to biased or manipulated responses.

These attacks underscore the importance of robust security measures and vigilance in handling external content in LLM-powered applications to mitigate potential risks and vulnerabilities.

How might the techniques used in retrieval poisoning attacks be adapted to target closed-source LLMs or other AI systems that rely on external knowledge sources?

Adapting retrieval poisoning techniques to target closed-source LLMs or other AI systems that rely on external knowledge sources involves several considerations:

Black-Box Analysis: Conducting black-box analysis to infer the behavior and response patterns of the closed-source LLMs or AI systems by observing their outputs to crafted inputs.

Transfer Learning: Leveraging transfer learning techniques to adapt attack sequences generated for open-source LLMs to closed-source models by fine-tuning the sequences based on the responses obtained.

Feature Engineering: Identifying key features or patterns in the external content that are likely to influence the closed-source models' responses and crafting attack sequences tailored to exploit these features.

Gradient Estimation: Employing gradient estimation methods to approximate the gradients of the closed-source models and guide the generation of effective attack sequences that manipulate the model's responses.

Stealthy Injection: Ensuring the imperceptibility of attack sequences by embedding them in external content in a stealthy manner that evades detection by the closed-source models' security mechanisms.

Dynamic Adaptation: Continuously adapting and evolving the attack techniques based on the closed-source models' responses and behavior to circumvent any defensive measures implemented by the systems.

By applying these strategies, attackers can potentially adapt retrieval poisoning techniques to target closed-source LLMs or other AI systems reliant on external knowledge sources, highlighting the need for robust security measures and defenses to safeguard against such attacks.