SoK: Security of Autonomous LLM Agents in Agentic Commerce
2026-04-20T07:23:32Z•4b090d43cea50262271c992e466ab540eebc58564745ab7d19da779a6f4c7691
LinuxArenaMPCPII-detectionagent-benchmarksagent-paymentsagentic-commerceauthorization-gapsautonomous-llm-agentscloud-logsencrypted-routinggraph-adversarial-attacks`,`symbolic-verification`,`cumulative‑sguardrails-failureharmful-skillsiot-healthcarelog-injectionmultimodal-misinformationpost-quantum-migrationprivacy-preserving-workflowprompt-injectionremote-code-executionsabotage-detectionsecure-inferenceskill-ecosystemssupply-chain-risksynthetic-media-detection
What happened
Collection of recent research highlighting systemic security risks introduced by autonomous LLM agents, agent ecosystems, and related AI tooling. Key contributions: a unified security framework for agentic commerce identifying 12 cross‑layer attack vectors (agent integrity, authorization, inter-agent trust, market manipulation, compliance) and proposing layered defenses; LogJack demonstrating effective indirect prompt‑injection via cloud logs that yields high verbatim command execution and remote code execution rates while major cloud guardrails largely fail; measurement and benchmark studies—
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_cr
- Record identifier
- 4b090d43cea50262271c992e466ab540eebc58564745ab7d19da779a6f4c7691
- Enrichment time
- 2026-04-20T07:23:32Z
- AI-assisted enrichment
- Yes
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