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12 posts

• 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.

  Jul 10, 2026 agent-security multi-agent orchestration trust-model privilege-escalation autogen langchain architecture llm

• Non-Human Identity Security for AI Agents: Credential Scoping, Token Lifecycle, and Agent Impersonation

  AI agents are becoming major credential holders and API consumers. Non-human identity (NHI) security treats agent tokens with the same rigor as human credentials — and getting this wrong creates exploitable privilege escalation paths that traditional IAM never anticipated.

  Jul 9, 2026 agent-security credential-management least-privilege iam identity threat-modeling multi-agent zero-trust

• Shadow Prompting: How Hidden System Instructions Hijack AI Behavior

  Hidden system-level instructions can silently override how an AI application behaves — without the user ever seeing the manipulation. This post explores what makes shadow prompting possible, how it differs from ordinary prompt injection, and what architectural choices actually reduce the risk.

  Jul 1, 2026 prompt-injection context-window rag-security llm-security multi-agent agent-security

• Multi-Agent Trust Escalation: How Subagents Inherit and Abuse Orchestrator Permissions

  Multi-agent architectures introduce a trust escalation problem analogous to privilege escalation in OS security. When orchestrators delegate permissions to subagents, the attack surface multiplies. This post maps the attack classes, draws the OS and service-mesh analogies, and offers concrete defenses.

  Jun 29, 2026 agent-security multi-agent trust privilege-escalation least-privilege threat-modeling

• AgentBridge Attack Surface Analysis: When the Mesh Layer Becomes the Threat

  Protocol translation bridges for AI agents look like infrastructure plumbing. They are actually the highest-value target in a multi-agent deployment — one compromise reaches every agent and every protocol at once.

  Jun 20, 2026 agent-security threat-modeling multi-agent protocols architecture

• The Inbox Is the New Attack Surface: What Gemini Spark Reveals About Personal AI Agent Security

  Google's personal AI agent has ambient authority over your Gmail, Calendar, and Drive. Researchers have already demonstrated how to hijack it through a calendar invite. Infrastructure defenses don't fix this.

  May 22, 2026 agent-security prompt-injection threat-modeling defense-patterns multi-agent

• When Your Agent Forgets the Right Things: Skill Libraries as Emergent Defense Against Memory Poisoning

  A new RL framework for agent skill libraries creates an unexpected security property: skills that lead to task failures get naturally retired. Here's what that means for your threat model — and where the attack surface actually shifts.

  May 14, 2026 agent-security threat-modeling defense-patterns multi-agent capability-theft

• Safe in Isolation, Dangerous Together: The Multi-Turn Blind Spot in Your Safety Filter

  Decompositional jailbreaks split a harmful request across innocuous-looking turns. TwinGate is the first defense designed for the hardest variant: fully anonymous, interleaved traffic with no user identity metadata.

  May 11, 2026 prompt-injection defense-patterns threat-modeling agent-security multi-agent

• 7.1%: What Happens When You Actually Measure Multi-Agent Safety

  TrinityGuard tested real multi-agent system configurations against a structured, OWASP-grounded taxonomy of 20 risk types. The average safety pass rate was 7.1%. Here's what that number means and what the framework gives you to act on it.

  May 6, 2026 multi-agent threat-modeling defense-patterns evaluation agent-security

• When AI Agents Talk in Embeddings, Text-Level Safety Filters Go Blind

  RecursiveMAS replaces inter-agent text communication with latent-space embeddings for efficiency. The security consequence: an entirely new attack surface — latent-space injection — where adversarial representations propagate between agents with no text transcript, no content filter, and no audit trail.

  May 2, 2026 agent-security multi-agent prompt-injection threat-modeling latent-space

• Safe Agents, Unsafe Systems: The Non-Compositionality Problem in Multi-Agent Security

  A 24-author paper from Oxford, CMU, MIT, and the Turing Institute argues that individually safe AI agents can compose into unsafe systems — and that securing each agent in isolation misses the point entirely.

  May 1, 2026 multi-agent agent-security threat-modeling prompt-injection alignment

• What Red-Teaming Misses When Agents Talk to Each Other

  Microsoft Research red-teamed a live 100+ agent platform and found four attack classes — worms, amplification, trust capture, proxy chains — that only emerge at network scale. Single-agent benchmarks miss all of them.

  May 1, 2026 agent-security prompt-injection multi-agent threat-modeling red-teaming