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research(security): attack/defense landscape for agentic AI — taxonomy for #2417 and #2420 (arXiv:2603.11088) #2426

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Description

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Source

arXiv:2603.11088 — The Attack and Defense Landscape of Agentic AI: A Comprehensive Survey (2026-03-11)

Technique

First systematic survey of agent attack vectors and defenses across the full agent stack:

Attack taxonomy:

  • Prompt injection (direct + indirect)
  • Tool hijacking (malicious MCP server responses)
  • Privilege escalation (agent acquiring unintended capabilities)
  • Memory poisoning (corrupting stored context)
  • Multi-agent cascade attacks (compromising one agent to propagate to others)

Defense taxonomy:

  • Least-privilege sandboxing
  • Containerized tool execution
  • Input/output sanitization layers
  • Per-tool access tokens (ephemeral credentials)
  • Cross-agent message signing

Applicability to Zeph

Grounds #2417 (formal security model): The paper's structured taxonomy directly maps onto the Task/Action/Source/Data alignment framework. Use this as the reference survey when implementing the formal model.

Grounds #2420 (MCP tool trust metadata): The per-tool access token pattern is exactly what #2420 proposes — this paper validates the approach with empirical evidence across multiple agent systems.

ContentSanitizer / ExfiltrationGuard review: The memory poisoning and tool hijacking attack classes should be audited against current Zeph defenses. The survey may reveal gaps not yet covered by ContentIsolation or ExfiltrationGuard.

Confused-deputy analysis: The survey explicitly covers multi-agent privilege escalation — relevant when Zeph acts as both ACP server and MCP client simultaneously (the confused-deputy scenario from arXiv:2603.12230).

Implementation sketch

Use the taxonomy as a checklist in the #2417 implementation:

  1. Map each attack class to an existing or missing Zeph defense
  2. Identify uncovered classes → new [security] config options or guardrail rules
  3. File sub-issues for any critical uncovered classes

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P2High value, medium complexityresearchResearch-driven improvement

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