Sefz discovers specification violations in 29.9% of 402 real-world agent skills by translating guardrails into reachability goals and guiding LLM mutations with a multi-armed bandit.
The landscape of prompt injection threats in llm agents: From taxonomy to analysis
8 Pith papers cite this work. Polarity classification is still indexing.
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PACT achieves perfect security and utility under oracle provenance by enforcing argument-level trust contracts based on semantic roles and cross-step provenance tracking, outperforming invocation-level monitors in AgentDojo evaluations.
The Moltbook Observatory Archive is the first large-scale dataset from a social network populated exclusively by autonomous AI agents, covering 78 days with 2.6 million posts and 1.2 million comments.
AI agent providers face an exhaustive inventory requirement for actions and data flows, as high-risk systems with untraceable behavioral drift cannot meet the AI Act's essential requirements.
LoopTrap is an automated red-teaming framework that crafts termination-poisoning prompts to amplify LLM agent steps by 3.57x on average (up to 25x) across 8 agents.
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.
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.
AI agents require distinct regulation as AI systems under the EU AI Act with orchestration-layer oversight and a risk-based traffic light authorization system in contract law to preserve human accountability.
citing papers explorer
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No Attack Required: Semantic Fuzzing for Specification Violations in Agent Skills
Sefz discovers specification violations in 29.9% of 402 real-world agent skills by translating guardrails into reachability goals and guiding LLM mutations with a multi-armed bandit.
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The Granularity Mismatch in Agent Security: Argument-Level Provenance Solves Enforcement and Isolates the LLM Reasoning Bottleneck
PACT achieves perfect security and utility under oracle provenance by enforcing argument-level trust contracts based on semantic roles and cross-step provenance tracking, outperforming invocation-level monitors in AgentDojo evaluations.
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The Moltbook Observatory Archive: an incremental dataset of agent-only social network activity
The Moltbook Observatory Archive is the first large-scale dataset from a social network populated exclusively by autonomous AI agents, covering 78 days with 2.6 million posts and 1.2 million comments.
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AI Agents Under EU Law
AI agent providers face an exhaustive inventory requirement for actions and data flows, as high-risk systems with untraceable behavioral drift cannot meet the AI Act's essential requirements.
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LoopTrap: Termination Poisoning Attacks on LLM Agents
LoopTrap is an automated red-teaming framework that crafts termination-poisoning prompts to amplify LLM agent steps by 3.57x on average (up to 25x) across 8 agents.
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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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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.
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A pragmatic approach to regulating AI agents
AI agents require distinct regulation as AI systems under the EU AI Act with orchestration-layer oversight and a risk-based traffic light authorization system in contract law to preserve human accountability.