paperarXivTrust 82 · PrimaryPublished 3d agoLive · 2d ago
A Lifecycle and Application-Stack Survey of Large Language Model Vulnerabilities: Attacks, Risks, Defenses, and Open Problems
Large language models are no longer only text generators. They are increasingly embedded in retrieval pipelines, enterprise assistants, coding environments, robotic systems, security-operation workflows, and autonomous agents that can read private data, call tools, write files, execute code, and act across organizational boundaries. This shift changes the security problem: risks do not arise from the model weights alone, but from the full lifecycle and application stack through which data, prompts, model outputs, tools, memories, and user authority interact. This paper systematizes the literat
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newsIEEE Rolls Out Large Language Models Virtual Training CoursenewsPrompt injection is exploiting enterprise AI's biggest design flaws by targeting agents, RAG pipelines and model routersnewsDefending against Prompt Injection with Structured Queries (StruQ) and Preference Optimization (SecAlign)news"Dangerous" AI models are coming no matter whatnewsBook Review: Domain-Specific Small Language Models by Guglielmo Iozzia
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newsPrompt injection is exploiting enterprise AI's biggest design flaws by targeting agents, RAG pipelines and model routersnewsDefending against Prompt Injection with Structured Queries (StruQ) and Preference Optimization (SecAlign)repoPipelex/pipelexnewsBook Review: Domain-Specific Small Language Models by Guglielmo Iozziarepohuhusmang/Awesome-LLMs-for-Vulnerability-DetectionnewsIEEE Rolls Out Large Language Models Virtual Training CoursenewsA system-level approach to prompt injection: separating instruction and data channels in LLM agents [P]news"Dangerous" AI models are coming no matter whatrepoaallan/vera
