SeClaw provides spec-driven synthesis of security tasks and an execution-based docker testbed for evaluating unsafe behaviors in autonomous LLM agents.
Canonical reference
SAGA: A security architecture for governing AI agentic systems
Canonical reference. 83% of citing Pith papers cite this work as background.
12
Pith papers citing it
Background
83% of classified citations
citation-role summary
background 6
citation-polarity summary
years
2026 12roles
background 6representative citing papers
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.
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.
AgenticRei applies the Rei deontic framework in OWL for runtime governance of agentic AI, handling obligations and waivers that standard policy engines lack.
Introduces a compositional governance framework defining delegation types, resource scope attenuation, and an overlay operator for agentic AI authorization policies.
MolTrust deploys a W3C VC+DID trust infrastructure for AI agents with kernel-layer authorization, cross-protocol interoperability, and layered Sybil resistance, operational since March 2026 across eight verticals.
The EU AI Act narrows accountability for multi-agent AI in critical infrastructure by excluding safety components from key explanation and impact assessment rights, and the paper proposes AgentGov-SC, a three-layer architecture with 25 measures to address this through traceability to existing AI and
Bounded autonomy using typed action contracts and consumer-side execution lets LLMs safely operate enterprise systems, achieving 23 of 25 tasks with zero unsafe executions versus 17 for unconstrained AI across 25 trials.
The paper systematizes security for LLM agents in agentic commerce into five threat dimensions, identifies 12 cross-layer attack vectors, and proposes a layered defense architecture.
ACP is a temporal admission control protocol that combines static risk scoring with anomaly accumulation and cooldowns to limit harmful agent behavior over time, reducing approvals from 100% to 0.4% in tested workloads.
Proposes a protocol DSL for AI-SDLC with formal syntax, semantics, and invariants, plus failure-rate bounds and a 2+N team pattern.
citing papers explorer
-
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.
-
From Specification to Deployment: Empirical Evidence from a W3C VC + DID Trust Infrastructure for Autonomous Agents
MolTrust deploys a W3C VC+DID trust infrastructure for AI agents with kernel-layer authorization, cross-protocol interoperability, and layered Sybil resistance, operational since March 2026 across eight verticals.
-
SoK: Security of Autonomous LLM Agents in Agentic Commerce
The paper systematizes security for LLM agents in agentic commerce into five threat dimensions, identifies 12 cross-layer attack vectors, and proposes a layered defense architecture.
- Like a Hammer, It Can Build, It Can Break: Large Language Model Uses, Perceptions, and Adoption in Cybersecurity Operations on Reddit