Bridging the AI Security Gap: From Vulnerabilities to Principaled Defenses

Current Large Language Model (LLM) agents are more powerful than ever, yet they remain fundamentally vulnerable to attacks like memory poisoning and context manipulation. Research shows that even high-performing models can fail to defend against prompt injection 85% of the time.In our latest technical analysis, we cover:Vulnerabilities: Why the "unreliable loop" of LLM agents creates entry points for data leakage.AgentSandbox: A framework integrating classic security principles such as Least Privilege and Complete Mediation.Defense-in-Depth: Why task-specific "ephemeral" agents are the future of secure AI workflows.Securing the agentic future isn't just about better models; it's about better architectures.Source Paper: https://arxiv.org/pdf/2505.24019 All my links: https://linktr.ee/learnbydoingwithsteven#LLMs #AISecurity #AgenticAI #CyberSecurity #CloudSecurity #TechDiscussion #LearnByDoingWithSteven #StevenDataTalk #SecurityArchitecture #AIInnovation