ClawGuard detects LLM agent workflow hijacking by capturing and classifying electromagnetic emanations from hardware with 0.9945 AUC, 100% true-positive rate, and 1.16% false-positive rate on a 7.82 TB RF dataset.
ACE: A Security Architecture for LLM-Integrated App Systems
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
fields
cs.CR 6years
2026 6verdicts
UNVERDICTED 6representative citing papers
ARGUS defends LLM agents from context-aware prompt injections by tracking information provenance and verifying decisions against trustworthy evidence, reducing attack success to 3.8% while retaining 87.5% task utility.
GAAP guarantees confidentiality of private user data for AI agents by enforcing user-specified permissions deterministically through persistent information flow tracking, without trusting the agent or requiring attack-free models.
Parallax enforces structural separation between AI thinking and acting via independent multi-tier validation, information flow control, and state rollback, blocking 98.9% of 280 adversarial attacks with zero false positives even when the reasoning system is fully compromised.
AI agents should shift from on-the-fly plan synthesis to invoking pre-engineered, tested, and reusable workflows stored in an AI Workflow Store to gain reliability and security.
ClawLess introduces a formal fine-grained security model for AI agents with runtime-adaptive policies enforced via user-space kernel and BPF syscall interception.
citing papers explorer
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ClawGuard: Out-of-Band Detection of LLM Agent Workflow Hijacking via EM Side Channel
ClawGuard detects LLM agent workflow hijacking by capturing and classifying electromagnetic emanations from hardware with 0.9945 AUC, 100% true-positive rate, and 1.16% false-positive rate on a 7.82 TB RF dataset.
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ARGUS: Defending LLM Agents Against Context-Aware Prompt Injection
ARGUS defends LLM agents from context-aware prompt injections by tracking information provenance and verifying decisions against trustworthy evidence, reducing attack success to 3.8% while retaining 87.5% task utility.
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An AI Agent Execution Environment to Safeguard User Data
GAAP guarantees confidentiality of private user data for AI agents by enforcing user-specified permissions deterministically through persistent information flow tracking, without trusting the agent or requiring attack-free models.
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Parallax: Why AI Agents That Think Must Never Act
Parallax enforces structural separation between AI thinking and acting via independent multi-tier validation, information flow control, and state rollback, blocking 98.9% of 280 adversarial attacks with zero false positives even when the reasoning system is fully compromised.
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Engineering Robustness into Personal Agents with the AI Workflow Store
AI agents should shift from on-the-fly plan synthesis to invoking pre-engineered, tested, and reusable workflows stored in an AI Workflow Store to gain reliability and security.
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ClawLess: A Security Model of AI Agents
ClawLess introduces a formal fine-grained security model for AI agents with runtime-adaptive policies enforced via user-space kernel and BPF syscall interception.