arXiv AI

A Hybrid Insider Threat Detection Framework Combining Multi-Agent Simulation, Layered SIEM Correlation, and Theory-of-Mind Reasoning

The paper introduces a hybrid insider threat detection framework that combines multi-agent simulation, layered SIEM correlation, trust‑adaptive thresholds, behavioral and communication forensics, and Theory‑of‑Mind reasoning. It evaluates four variants—Layered SIEM‑Core, Cognitive‑Enriched SIEM, Evidence‑Gated SIEM, and an Enron‑calibrated version—showing progressively higher actor‑level F1 scores and reduced false positives, especially with evidence gating. Domain‑shift tests reveal that an Enron‑trained email classifier does not transfer to other domains, but fine‑tuning achieves high F1, and scalability tests confirm stable performance up to 1,000 agents.

arXiv AI

Sep 25

On the Effectiveness of Kernel-Level Evidence for Agent Security

The paper introduces the Agent Cross‑Layer Evidence (ACE) corpus, pairing application‑level telemetry with kernel‑level syscall traces to study agent security. It shows that kernel evidence alone is discriminative and that combining it with application‑level data outperforms either layer alone, revealing complementary signals. The study also demonstrates that this cross‑layer approach generalizes to unseen attack families and works across different agent runtimes.

By Spencer King, Zhilu Zhang, Mikhail Kuznetsov, Kay Liu, Baris Coskun, Wei Ding

llms agents benchmarks

arXiv AI

Jun 17

An AI Security Agent for Banking: Multi-Vector Fraud and AML Detection Across Retail and Corporate Accounts

arXiv:2606. 17555v1 Announce Type: cross Abstract: Banks simultaneously face signature-based fraud (card-not-present attacks, account takeover, ATM cloning) and behavioural financial crime (structuring, layering, mule networks, business email compromise) -- two threat families with fundamentally different detection requirements.

By Joseph Walusimbi, Joshua Benjamin Ssentongo

agents

arXiv Machine Learning

Jul 31

Cybersecurity Detection Classification with Reasoning-enabled Language Models

arXiv:2607. 28460v1 Announce Type: new Abstract: A major issue in Security Operations Centers (SOCs) is alert fatigue, as the number of detections reported is more than staff can triage in a given day.

By Amol Khanna, Manu Nandan, Cristian Viorel Popa, Joan Pujol-Roig, Diana Bolocan, Laura Vasilie, Alexandru Apostu, Chase Helwig, Mihaela Gaman, Michael Brautbar, Edward Raff, Chase Midler, Sven Krasser

llms reinforcement-learning

arXiv AI

Jun 3

FORGE: Multi-Agent Graduated Exploitation and Detection Engineering

arXiv:2606. 03453v1 Announce Type: cross Abstract: Vulnerability disclosure volumes now far exceed organizational assessment capacity, yet three adjacent research communities (proof-of-concept generation, vulnerability prioritization, and detection rule engineering) operate largely in isolation.

By Farooq Shaikh

llms agents

arXiv AI

Sep 23

Connecting the Dots in Agentic AI Security: A Cross-Dimensional Threat Taxonomy, Evaluation Maturity, and Open Challenges

arXiv:2609.23894v1 Announce Type: cross Abstract: Agentic AI extends LLM security beyond generated content to persistent state, autonomous actions, tool use, and interactions with humans and other ag...

By Heewon Baek, Alsharif Abuadbba, Kristen Moore, Hyoungshick Kim, Surya Nepal

llms agents benchmarks safety

arXiv AI

Jun 16

Benign in Isolation, Harmful in Composition: Security Risks in Agent Skill Ecosystems

arXiv:2606. 15242v1 Announce Type: cross Abstract: Skills are becoming the capability layer through which LLM agents turn plans into actions, but their use introduces security risks such as data leakage, unauthorized operations, and tool misuse.

By Yi Xie, Jiawei Du, Yu Cheng, Jiuan Zhou, Zhaoxia Yin

llms agents benchmarks

arXiv AI

Aug 21

MaliciousSkillBench: A Comprehensive Benchmark for Malicious Agent Skill Detection

arXiv:2608. 19901v1 Announce Type: cross Abstract: Agent Skills extend LLM agents with reusable instruction packages that may also include scripts, resources, and service configuration.

By Yue Wang, Yi Liu, Gelei Deng, Ying Zhang, Yuekang Li, Zhenyu Chen, Leo Zhang

llms agents benchmarks

arXiv AI

6d ago

Coding Agents Aren't Enough! Evaluating an Enterprise Security Brain for Agentic Cloud Investigations

The article evaluates the Sola Security Brain, a purpose-built security intelligence layer, against a general-purpose coding agent (Claude Code) on 28 cloud‑security investigation tasks. The Sola Security Brain achieved 0.693 coverage versus 0.387 for the coding agent, a 79.2% relative gain, and outperformed the agent on 25 of 28 tasks while incurring far lower reasoning and cost per unit of coverage. The study also identifies a ‘sample‑and‑generalise’ pattern where the live agent reports universal negatives based on limited sampling, illustrating a potential efficiency trade‑off in cloud investigations.

By Leon Goldberg, Gal Engelberg, Eden Yavin, Elad Elouz, Ariel Zadok, Konstantin Koutsyi

llms agents

arXiv AI

Jun 4

Unpredictable Safety: Domain-Dependent Compliance and the Transparency Gap in Open-Weight LLMs

arXiv:2606. 04035v1 Announce Type: cross Abstract: We present a systematic study of domain-dependent safety behavior in open-weight LLMs: 7 standardized experiments across 7 ethical domains, testing 5 models (12B--70B) in 4,200 interactions with dual-judge validation.

By Zacharie Bugaud

llms safety

arXiv AI

Aug 24

ClawSentry: A Progressive Multi-Tier Security Monitor for Safeguarding Autonomous LLM Agents

ClawSentry is an open‑source, framework‑agnostic security supervision gateway designed to protect autonomous large language model (LLM) agents from progressive risks that can arise at four points in the agent control loop: skill admission, invocation‑time intent, execution‑time effect, and post‑action consequence. It introduces a multi‑tier decision engine—deterministic L1, rule‑anchored L2, and read‑only L3—alongside a First‑Use Skill Package Review (FSPR) and an Agent Harness Protocol (AHP) that applies a single policy across multiple agent runtimes without modifying their internals. Evaluation on SkillInject and the SkillsSafety benchmark shows that ClawSentry significantly reduces contextual adversarial skill risk (ASR) while maintaining high task success rates (TSR).

By Kai Wang, Zeming Wei, BiaoJie Zeng, Chang Jin, An Wang, Xiaokun Luan, Zhixiao Lin, Jingjing Qu, Xia Hu, Xingcheng Xu

llms agents multimodal benchmarks

arXiv AI

4d ago

SKILLLITE: Evidence-Guided Malicious Skill Auditing with Compact LLMs

arXiv:2609.36879v1 Announce Type: cross Abstract: As LLM-based agents perform increasingly complex tasks, Agent Skills have emerged as a flexible mechanism for extending their capabilities. An Agent...

By Haoran Ou, Gelei Deng, Xuanye Zhang, Wenbo Guo, Tianwei Zhang, Kwok-Yan Lam

llms agents efficiency

arXiv AI

Jun 16

SkillVetBench: LLM-as-Judge for Multi-Dimensional Security Risk Evaluation in Open-Source LLM Agent Skills

arXiv:2606. 15899v1 Announce Type: cross Abstract: Open-source LLM agent ecosystems are growing rapidly, yet the security of community-contributed skills - modular tool definitions that extend agent capabilities - remains largely unvetted.

By Ismail Hossain, Sai Puppala, Md Jahangir Alam, Tanzim Ahad, Sajedul Talukder

llms agents benchmarks

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