arXiv AI August 17, 2026 By Aryan Luthra, Kshitij Jain, Siddharth Arya, Bobby Filar, Anna Bertiger

Evaluating Agentic Learning Harness Capabilities Without Labels via the Scaling Hypothesis

arXiv:2608. 13608v1 Announce Type: new Abstract: Agentic "Continual Learning Harnesses", systems that pair an LLM with retrieval or memory to improve from feedback without retraining, have shown growing value in cybersecurity.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Aug 3

Distilling Knowledge from Large Language Models into Lightweight Reinforcement Learning Agents for Autonomous Cyber Operations

arXiv:2607. 28826v1 Announce Type: new Abstract: Autonomous Cyber Operations (ACO) are increasingly important for defending enterprise networks as cyber threats continue to evolve in sophistication.

By Konur Tholl, Fran\c{c}ois Rivest, Mariam El Mezouar, Adrian Taylor, Ranwa Al Mallah

llms agents reinforcement-learning fine-tuning efficiency safety

arXiv AI

Sep 23

SelfOp: An Optimization Algorithm for Self-Improving Security Agents

arXiv:2609.22792v1 Announce Type: cross Abstract: LLM agents are increasingly used for security tasks: vulnerability discovery, exploit reproduction, and patch generation. Improving them at the model...

By Saad Ullah, Yigitcan Kaya, Christopher Kruegel, Giovanni Vigna, Gianluca Stringhini

llms agents benchmarks

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

arXiv AI

Jul 7

Governed Individuation: Cryptographically Decoupling an Agent's Learning from Its Authority

arXiv:2607. 04613v1 Announce Type: new Abstract: Autonomous agents are moving from sandboxed text generators to operators of code, data, and physical infrastructure, and they increasingly learn while deployed.

By Xue Qin, Simin Luan, Cong Yang, Zhijun Li

agents benchmarks safety

arXiv AI

Sep 30

Meta-SecAlign: Training LLMs against Prompt Injection for Robust Agents

arXiv:2507.02735v4 Announce Type: replace-cross Abstract: Prompt injection attacks, where untrusted data contains an injected prompt to manipulate the system, have been listed as the top security thr...

By Sizhe Chen, Arman Zharmagambetov, David Wagner, Chuan Guo

llms agents fine-tuning benchmarks

Hugging Face Trending Papers

Aug 5

Trident : How to Break Deep Reinforcement Learning Cyber Defenses (Agentic)

Autonomous cyber defense systems based on Deep Reinforcement Learning (DRL) have attracted significant research attention, yet remain evaluated almost exclusively against static, heuristic red agents, leaving their robustness against adaptive threats critically understudied. Meanwhile, recent advances in Reinforcement Learning with Verifiable Rewards (RLVR) have improved LLM reasoning, but their integration into cybersecurity remains elusive due to the absence of suitable benchmark environments and interaction datasets.

llms agents reinforcement-learning benchmarks

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Related stories

Distilling Knowledge from Large Language Models into Lightweight Reinforcement Learning Agents for Autonomous Cyber Operations

SelfOp: An Optimization Algorithm for Self-Improving Security Agents

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

Governed Individuation: Cryptographically Decoupling an Agent's Learning from Its Authority

Meta-SecAlign: Training LLMs against Prompt Injection for Robust Agents

Trident : How to Break Deep Reinforcement Learning Cyber Defenses (Agentic)