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Securing Large Language Models: Threats, Vulnerabilities and Responsible Practices
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Large language models (LLMs) have significantly transformed the landscape of Natural Language Processing (NLP). Their impact extends across a diverse spectrum of tasks, revolutionizing how we approach language understanding and generations. Nevertheless, alongside their remarkable utility, LLMs introduce critical security and risk considerations. These challenges warrant careful examination to ensure responsible deployment and safeguard against potential vulnerabilities. This research paper thoroughly investigates security and privacy concerns related to LLMs from five thematic perspectives: security and privacy concerns, vulnerabilities against adversarial attacks, potential harms caused by misuses of LLMs, mitigation strategies to address these challenges while identifying limitations of current strategies. Lastly, the paper recommends promising avenues for future research to enhance the security and risk management of LLMs.
Forward citations
Cited by 4 Pith papers
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LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systems
A systematic review that categorizes LLM threats, severity scores, and mitigations across development and operation life cycles and multiple deployment scenarios.
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Invisible Injections: Exploiting Vision-Language Models Through Steganographic Prompt Embedding
Steganographic prompt injection is reported to covertly manipulate vision-language models with up to 31.8% success, but the evidence is not reproducible.
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Beyond the Protocol: Unveiling Attack Vectors in the Model Context Protocol (MCP) Ecosystem
Malicious MCP servers can be uploaded to popular registries, are hard for users to spot, and can manipulate LLM agents into harmful actions.
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Adversarial Prompting Framework for AI Safety Assessment
An adversarial prompt testing framework with a five-level attack taxonomy and a composite harmfulness score is proposed; the paper claims encoded prompts bypass safety filters most often.
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