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newsVentureBeatTrust 60Published 5d agoLive · 5d ago

Prompt injection is exploiting enterprise AI's biggest design flaws by targeting agents, RAG pipelines and model routers

In the past two years, businesses have been trying to fit large language models (LLMs) into support, analytics, development, and internal automation like never before. Along with the increasing adoption of AI technology , another trend is gaining momentum — cybercriminals are taking advantage of the disconnect between assumptions about LLMs and their actual c

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paperFrom Tokens to States: LLMs as a Special Case of World Models and the Continuous Path Beyondrepodchatterjee01-prog/analyticaospaperDirect Causation in International Humanitarian Law and the Challenge of AI-Mediated Civilian Cyber OperationspaperPolicyGuard: A Dialogue-Grounded Sub-Agent Verifier for Policy Adherence in LLM AgentspaperManufactured Confidence: How Memory Consolidation Turns Hearsay into Confident FactspaperEvalSafetyGap: A Hybrid Survey and Conceptual Framework for LLM Evaluation-Safety FailurespaperMulti-Agentic System Leveraging Open-Source LLMs to Mitigate Disinformation ThreatspaperEntity Binding Failures in Tool-Augmented AgentspaperLinguistic Firewall: Geometry as Defense in Multi-Agent Systems RoutingpaperWords Speak Louder Than Code: Investigating Cognitive Heuristics in LLM-Based Code Vulnerability DetectionpaperWhen the Database Fails: Prompting LLM Dialogue Agents for Safe Recovery in Task-Oriented DialoguepaperFLARE-AI: Flaw Reporting for AIpaperA Lifecycle and Application-Stack Survey of Large Language Model Vulnerabilities: Attacks, Risks, Defenses, and Open ProblemspaperTheory of Mind and Persuasion Beyond Conversation: Assessing the Capacity of LLMs to Induce Belief States via Planning and ActionpaperBehavior-Adaptive Conversational Agents: Toward a Fluid Personality FrameworkpaperAgentic generation of verifiable rules for deterministic, self-expanding reaction classificationpaperConversable Complexity: Agentic LLM Collectives as Interpretable SubstratespaperDistill to Detect: Exposing Stealth Biases in LLMs through Cartridge DistillationrepoAgustiPuigserver/opus-prompt-architectrepopromptfoo/promptfoorepoProductive-Superintelligence/lllmrepoBigBoySlave/Agents-Prompts

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