A Lifecycle and Application-Stack Survey of Large Language Model Vulnerabilities: Attacks, Risks, Defenses, and Open Problems
arXiv:2606.31639v1 Announce Type: cross Abstract: 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 autho
The rapid integration of Large Language Models (LLMs) into critical enterprise and autonomous systems necessitates a comprehensive understanding of their expanded attack surface beyond mere text generation.
A strategic reader must recognize that LLM security is no longer confined to model weights but encompasses the entire application stack, data flows, and operational boundaries, introducing new systemic risks.
The scope of AI security shifts from isolated model vulnerabilities to a holistic lifecycle and application-stack perspective, impacting how these powerful systems are designed, deployed, and governed.
- · Cybersecurity firms specializing in AI/ML
- · Organizations developing secure LLM integration frameworks
- · AI red teaming and auditing services
- · Organizations with immature LLM security practices
- · Developers solely focused on model-level security
- · End-users of compromised LLM-powered systems
Increased investment in LLM-specific security tools and practices.
New regulatory frameworks emerging to mandate comprehensive security standards for AI agents and LLM-integrated systems.
The emergence of 'secure AI engineering' as a distinct and high-demand discipline within software development.
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Read at arXiv cs.AI