Tagged:
llm5 posts
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LLM Security Monitoring in Production: Anomaly Detection, Audit Logging, and Intrusion Detection for AI Systems
Guardrails block known bad inputs. Incident response handles breaches after they happen. This post covers the gap: building LLM-specific observability that detects attacks while they are occurring — anomalous outputs, prompt injection signatures, unusual tool-call patterns, and model drift from baseline.
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Multi-Agent Orchestration Security: Trust, Delegation, and Inter-Agent Attack Surfaces
When Agent A delegates a task to Agent B, the security properties of the entire system reduce to the weakest trust model in the chain. This post maps the attack taxonomy unique to orchestrator-worker architectures — privilege escalation, agent impersonation, confused deputy, tool-chain hijacking — and pairs each with concrete defenses grounded in published research.
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Zero-Trust Architecture for AI Agent Deployments: Never Trust, Always Verify — Even Your Own Agents
Zero-trust was designed for human users on networks. AI agents break its assumptions: they act autonomously, spawn child agents, inherit permissions, and run long-lived sessions. Here's how to adapt zero-trust principles specifically for multi-agent and LLM deployment contexts.
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LLM Output Watermarking: Provenance, Detection Limits, and Evasion
Token-level watermarks, cryptographic signing, and semantic embedding all promise attribution of AI-generated text — but paraphrase attacks, closed-pipeline requirements, and research-stage maturity constrain what any of them can actually guarantee. An honest practitioner's assessment.
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I Spent $1,500 Finding Out Which LLMs Can Hack a Real App — And What That Costs
A security researcher built a deliberately vulnerable app, ran nine frontier models against it as autonomous attack agents, and tracked every dollar. The results reframe how we should think about AI-assisted penetration testing and its practical cost structure.