APIOT is the first LLM framework to complete the full autonomous discovery-to-remediation cycle on bare-metal OT devices, reaching 90% success across 290 runs on Zephyr RTOS.
citation dossier
AgentSpec: Customizable runtime enforcement for safe and reliable LLM agents.arXiv preprint arXiv:2503.18666
19Pith papers citing it
20reference links
cs.CRtop field · 11 papers
UNVERDICTEDtop verdict bucket · 14 papers
why this work matters in Pith
Pith has found this work in 19 reviewed papers. Its strongest current cluster is cs.CR (11 papers). The largest review-status bucket among citing papers is UNVERDICTED (14 papers). For highly cited works, this page shows a dossier first and a bounded explorer second; it never tries to render every citing paper at once.
years
2026 19representative citing papers
AgentProp-Bench shows substring judging agrees with humans at kappa=0.049, LLM ensemble at 0.432, bad-parameter injection propagates with ~0.62 probability, rejection and recovery are independent, and a runtime fix cuts hallucinations 23pp on GPT-4o-mini but not Gemini-2.0-Flash.
A governed capability evolution framework with interface, policy, behavioral, and recovery checks reduces unsafe activations to zero in embodied agent upgrades while preserving task success rates.
Analysis of 17k LLM agent skills reveals 520 vulnerable ones with 1,708 leakage issues, primarily from debug output exposure, with a 10-pattern taxonomy and released dataset for future detection.
SOCpilot supplies a fixed verifier and public artifact that removes 466 non-compliant approval-gated actions from LLM plans on 200 real incidents while preserving task recall.
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.
Tool-mediated LLM agents with deterministic tools and a machine-checked Lyapunov certificate achieve stable control in cyber defense, reducing attacker game value by 59% on real attack graphs.
Semia synthesizes Datalog representations of agent skills via constraint-guided loops to enable reachability queries for semantic risks, finding critical issues in over half of 13,728 real skills with 97.7% recall on expert-labeled samples.
Alignment contracts define scope, allowed effects, budgets and disclosure rules as safety properties over finite effect traces, with decidable admissibility, refinement rules, and Lean-verified soundness under an observability assumption.
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.
Owner-Harm is a new threat model with eight categories of agent behavior that harms the deployer, and existing defenses achieve only 14.8% true positive rate on injection-based owner-harm tasks versus 100% on generic criminal harm.
PlanGuard cuts indirect prompt injection attack success rate to 0% on the InjecAgent benchmark by verifying agent actions against a user-instruction-only plan while keeping false positives at 1.49%.
No agent system can be accountable without auditability, which requires five dimensions (action recoverability, lifecycle coverage, policy checkability, responsibility attribution, evidence integrity) and mechanisms for detect/enforce/recover.
Independent evaluation of Claude Code auto mode finds 81% false negative rate on ambiguous authorization tasks due to unmonitored file edits.
A TEE-backed architecture isolates security-critical decisions in self-hosted AI agents to prevent host-level abuse from malicious inputs while maintaining allowed functionality.
Sovereign Agentic Loops decouple LLM reasoning from execution by emitting validated intents through a control plane with obfuscation and evidence chains, blocking 93% of unsafe actions in a cloud prototype while adding 12.4 ms latency.
Symbolic guardrails enforce 74% of specified safety policies in agent benchmarks and boost safety without hurting utility.
A multi-agent SDD framework with phase-level context-grounding hooks improves LLM-judged quality by 0.15 points and SWE-bench Lite Pass@1 by 1.7 percent while preserving near-perfect test compatibility.
citing papers explorer
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APIOT: Autonomous Vulnerability Management Across Bare-Metal Industrial OT Networks
APIOT is the first LLM framework to complete the full autonomous discovery-to-remediation cycle on bare-metal OT devices, reaching 90% success across 290 runs on Zephyr RTOS.
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Evaluating Tool-Using Language Agents: Judge Reliability, Propagation Cascades, and Runtime Mitigation in AgentProp-Bench
AgentProp-Bench shows substring judging agrees with humans at kappa=0.049, LLM ensemble at 0.432, bad-parameter injection propagates with ~0.62 probability, rejection and recovery are independent, and a runtime fix cuts hallucinations 23pp on GPT-4o-mini but not Gemini-2.0-Flash.
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Governed Capability Evolution: Lifecycle-Time Compatibility Checking and Rollback for AI-Component-Based Systems, with Embodied Agents as Case Study
A governed capability evolution framework with interface, policy, behavioral, and recovery checks reduces unsafe activations to zero in embodied agent upgrades while preserving task success rates.
-
Credential Leakage in LLM Agent Skills: A Large-Scale Empirical Study
Analysis of 17k LLM agent skills reveals 520 vulnerable ones with 1,708 leakage issues, primarily from debug output exposure, with a 10-pattern taxonomy and released dataset for future detection.
-
SOCpilot: Verifying Policy Compliance for LLM-Assisted Incident Response
SOCpilot supplies a fixed verifier and public artifact that removes 466 non-compliant approval-gated actions from LLM plans on 200 real incidents while preserving task recall.
-
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.
-
Stable Agentic Control: Tool-Mediated LLM Architecture for Autonomous Cyber Defense
Tool-mediated LLM agents with deterministic tools and a machine-checked Lyapunov certificate achieve stable control in cyber defense, reducing attacker game value by 59% on real attack graphs.
-
Semia: Auditing Agent Skills via Constraint-Guided Representation Synthesis
Semia synthesizes Datalog representations of agent skills via constraint-guided loops to enable reachability queries for semantic risks, finding critical issues in over half of 13,728 real skills with 97.7% recall on expert-labeled samples.
-
Alignment Contracts for Agentic Security Systems
Alignment contracts define scope, allowed effects, budgets and disclosure rules as safety properties over finite effect traces, with decidable admissibility, refinement rules, and Lean-verified soundness under an observability assumption.
-
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.
-
Owner-Harm: A Missing Threat Model for AI Agent Safety
Owner-Harm is a new threat model with eight categories of agent behavior that harms the deployer, and existing defenses achieve only 14.8% true positive rate on injection-based owner-harm tasks versus 100% on generic criminal harm.
-
PlanGuard: Defending Agents against Indirect Prompt Injection via Planning-based Consistency Verification
PlanGuard cuts indirect prompt injection attack success rate to 0% on the InjecAgent benchmark by verifying agent actions against a user-instruction-only plan while keeping false positives at 1.49%.
-
Auditable Agents
No agent system can be accountable without auditability, which requires five dimensions (action recoverability, lifecycle coverage, policy checkability, responsibility attribution, evidence integrity) and mechanisms for detect/enforce/recover.
-
Measuring the Permission Gate: A Stress-Test Evaluation of Claude Code's Auto Mode
Independent evaluation of Claude Code auto mode finds 81% false negative rate on ambiguous authorization tasks due to unmonitored file edits.
-
Constraining Host-Level Abuse in Self-Hosted Computer-Use Agents via TEE-Backed Isolation
A TEE-backed architecture isolates security-critical decisions in self-hosted AI agents to prevent host-level abuse from malicious inputs while maintaining allowed functionality.
-
Sovereign Agentic Loops: Decoupling AI Reasoning from Execution in Real-World Systems
Sovereign Agentic Loops decouple LLM reasoning from execution by emitting validated intents through a control plane with obfuscation and evidence chains, blocking 93% of unsafe actions in a cloud prototype while adding 12.4 ms latency.
-
Symbolic Guardrails for Domain-Specific Agents: Stronger Safety and Security Guarantees Without Sacrificing Utility
Symbolic guardrails enforce 74% of specified safety policies in agent benchmarks and boost safety without hurting utility.
-
Spec Kit Agents: Context-Grounded Agentic Workflows
A multi-agent SDD framework with phase-level context-grounding hooks improves LLM-judged quality by 0.15 points and SWE-bench Lite Pass@1 by 1.7 percent while preserving near-perfect test compatibility.
- ATBench: A Diverse and Realistic Agent Trajectory Benchmark for Safety Evaluation and Diagnosis