arXiv AI
Jun 16
A Survey on Agentic Security: Applications, Threats and Defenses
arXiv:2510. 06445v3 Announce Type: replace-cross Abstract: LLM-based agents are now used throughout cybersecurity.
When Agents Act on Web3: An Attack-Surface Survey of MCP, Skills, and Tool Calling
arXiv:2608. 17275v1 Announce Type: cross Abstract: AI agents increasingly act rather than merely read: across the Model Context Protocol (MCP) ecosystem, the share of deployed tools that modify external state has risen from 27% to 65% of tool use.
Related stories
arXiv AI
Jun 26
CyberChainBench: Can AI Agents Secure Smart Contracts Against Real-World On-Chain Vulnerabilities?
arXiv:2606. 26216v1 Announce Type: cross Abstract: We present CyberChainBench, a benchmark for evaluating LLM-based agents on smart contract security across three complementary tasks: vulnerability detection, exploit generation, and patch synthesis.
arXiv AI
Aug 12
On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models
arXiv:2608. 10530v1 Announce Type: cross Abstract: Large Language Models (LLMs) have undergone a shift from stateless conversational interfaces to autonomous agents capable of multi-step planning, tool invocation, code execution, and maintaining persistent memory.
arXiv AI
Jul 29
Cyber-Capable AI Agents: Vulnerabilities, Evaluation Containment, and Defensive Response
arXiv:2607. 25379v1 Announce Type: new Abstract: Cyber-capable AI agents combine language models with tools, memory, and execution en- vironments to perform multi-step offensive-security tasks.
arXiv AI
Jun 12
Who Pays the Price? Stakeholder-Centric Prompt Injection Benchmarking for Real-world Web Agents
arXiv:2606. 13385v1 Announce Type: cross Abstract: Web agents driven by large language models (LLMs) are increasingly deployed in real-world environments, where they operate over untrusted web content and execute actions with direct consequences.
arXiv AI
Aug 7
ChainClaw: A Layered Agent Framework for Reliable On-Chain Execution
arXiv:2608. 05790v1 Announce Type: new Abstract: General-purpose large language model agents have achieved strong performance on tool-augmented tasks, yet they rely on assumptions break down in blockchain environments.
arXiv AI
Jun 18
SafeClawBench: Separating Semantic, Audit-Evidence, and Sandbox Harm in Tool-Using LLM Agents
arXiv:2606. 18356v1 Announce Type: cross Abstract: Tool-using language-model agents introduce security failures that go beyond unsafe text: they can disclose protected objects, write persistent memory, send messages, modify databases, or trigger harmful code and tool effects.
Hugging Face Trending Papers
Aug 6
ChainClaw: A Layered Agent Framework for Reliable On-Chain Execution
General-purpose large language model agents have achieved strong performance on tool-augmented tasks, yet they rely on assumptions break down in blockchain environments. On-chain execution is stateful, adversarial, and economically irreversible, exposing three fundamental gaps: Reactivity, Irreversibility, and Observability.
arXiv AI
Jun 12
The Emergence of Autonomous Penetration Capabilities in Large Language Model-Powered AI Systems
arXiv:2606. 13079v1 Announce Type: cross Abstract: Nowadays, the autonomous execution of cyberattacks capable of causing substantial real-world harm is widely regarded as one of the critical red lines that frontier AI systems must not cross.
arXiv AI
23h ago
Runtime Governance for Agentic AI: Action-Boundary Control with Trusted Provenance and Fail-Closed Execution
arXiv:2608. 16891v1 Announce Type: new Abstract: Agentic AI systems request tool actions that can modify files, send messages, launch jobs, or change workflow state.
arXiv AI
6d ago
Agent Safety Should Be a Runtime Contract
arXiv:2608. 11274v1 Announce Type: cross Abstract: The dominant paradigm treats AI safety as a property to be instilled during model training via RLHF, DPO, or Constitutional AI.
arXiv AI
Jun 10
Toward Secure LLM Agents: Threat Surfaces, Attacks, Defenses, and Evaluation
arXiv:2606. 10749v1 Announce Type: cross Abstract: Large language model (LLM) agents are rapidly moving from conversational interfaces to software components that plan, invoke tools, maintain memory, and act on external environments.