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Trojaning Plugins of Large Language Models: The Philosopher's Stone
核心概念
Open-source Large Language Models can be exploited by Trojan adapters to induce malicious behavior, posing a significant threat to system security and information integrity.
摘要
The article explores the potential threat of Trojan adapters in Large Language Models (LLMs) and demonstrates how compromised adapters can control LLM outputs to execute malware or launch spear-phishing attacks. The study introduces two novel attacks, polished and fusion, to create Trojan adapters that outperform prior approaches in generating targeted misinformation and malicious tool usage. Experimental results show the effectiveness of these attacks in controlling LLM-driven agents and spreading disinformation. Despite efforts to defend against these attacks, current defenses are not entirely effective.
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arxiv.org
The Philosopher's Stone
統計資料
Open-source LLMs have gained popularity for domain-specific tasks.
Low-rank adapters (LoRAs) have increased dramatically in downloads over the past year.
Adversaries can exploit unregulated adapters to attack LLM users with carefully crafted triggers.
Fusion attack improves attack effectiveness by transforming existing popular adapters into Trojan ones.
Polished attack leverages a superior LLM as a teacher model to improve poisoned datasets quality.
引述
"Compromised adapters can control the loading models’ outputs when encountering inputs containing triggers."
"Our attacks provide higher attack effectiveness than the baseline for attracting downloads."
"More effective and generic countermeasures are in urgent need."
深入探究
How can organizations protect their systems from potential threats posed by Trojan adapters?
Organizations can take several measures to protect their systems from potential threats posed by Trojan adapters:
Regular Security Audits: Conduct regular security audits to identify any vulnerabilities in the system that could be exploited by malicious actors, including Trojan adapters.
Implement Access Controls: Implement strict access controls and authentication mechanisms to ensure that only authorized users have access to sensitive data and resources.
Update Software Regularly: Keep all software and applications up-to-date with the latest security patches to prevent known vulnerabilities from being exploited.
Use Intrusion Detection Systems (IDS): Deploy IDS tools to monitor network traffic for any suspicious activity or unauthorized access attempts.
Employee Training: Provide comprehensive training to employees on cybersecurity best practices, including how to recognize phishing attempts and other social engineering tactics used in conjunction with Trojan attacks.
Deploy Antivirus Software: Install and regularly update antivirus software on all devices connected to the organization's network to detect and remove malware, including Trojans.
Data Encryption: Encrypt sensitive data both at rest and in transit to prevent unauthorized access even if a Trojan adapter manages to infiltrate the system.
Incident Response Plan: Develop an incident response plan outlining steps for detecting, containing, eradicating, and recovering from a cyberattack involving a Trojan adapter.
What are the ethical implications of using such attacks on Large Language Models?
The use of attacks like Trojan adapters on Large Language Models (LLMs) raises significant ethical concerns:
Privacy Violation: By manipulating LLMs with malicious intent, individuals' privacy may be compromised as personal information could be exposed or misused without consent.
Misinformation Propagation: Using LLMs for spreading false information through targeted misinformation poses risks of deceiving individuals or influencing public opinion based on fabricated content.
Trust Erosion: Exploiting LLMs with malicious intent undermines trust in AI technologies as users may become wary of trusting automated systems due to potential misuse.
Security Threats: Attacks like Trojan adapters can lead not only t
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