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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AUTOATTACKER: A large language model guided system to implement automatic cyber-attacks
15 Pith papers cite this work. Polarity classification is still indexing.
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The first SoK on LLM-based AutoPT frameworks provides a six-dimension taxonomy of agent designs and a unified empirical benchmark evaluating 15 frameworks via over 10 billion tokens and 1,500 manually reviewed logs.
HIDBench unifies DARPA-E3, DARPA-E5, and NodLink datasets with a data pipeline to benchmark LLMs for host-based intrusion detection, showing high precision on simple logs but sharp drops in MCC and rises in false positives on complex noisy data.
LLM agents exhibit persistent attack-selection biases as fixed traits independent of success rates, with a bias momentum effect that resists steering and yields no performance gain.
Frontier models demonstrate in-context scheming by strategically deceiving in multiple agentic evaluations to achieve given goals.
APT-Agent automates penetration testing with LLMs using rectification and memory modules, achieving 84.29% end-to-end success on Metasploitable 2 versus lower rates for baselines.
PocketAgents introduces a manifest-driven library for LLM-based autonomous defense agents, evaluated in 18 closed-loop trials against a DarkSide-inspired attack where 13 trials produced validated blocking actions.
ASPO combines multi-agent LLM proposals with deterministic enforcement in a MAPE-K loop to select conflict-free, resource-feasible security patterns for IoT, delivering 100% safety invariants and 21-23% tail latency/energy reductions on testbed workloads.
CritBench evaluates five LLMs on 81 tasks in IEC 61850 environments, showing reliable performance on static analysis and single-tool reconnaissance but degradation on dynamic live-system tasks that require sequential reasoning, with domain-specific tools improving results.
RSA prompting enables LLMs to automatically create functional exploits for CVEs in Odoo ERP, succeeding on all tested cases in 3-5 rounds and removing the need for manual effort.
Expert-defined action plans for LLM agents achieve higher task completion in lateral-movement scenarios than fully autonomous or self-scaffolded modes, but failures remain common due to brittle commands and state handling.
Pen-Strategist fine-tunes Qwen-3-14B with RL on a pentesting reasoning dataset and pairs it with a CNN step classifier, reporting 87% better strategy derivation, 47.5% more subtask completions than baselines, and gains on CTFKnow and user studies.
Targeted prompting and system interventions enable local LLMs such as Llama 3.1 70B to exploit 83% of tested Linux privilege escalation vulnerabilities.
LanG presents a governance-aware agentic AI platform for unified security operations that reports strong performance on incident correlation, rule generation, attack reconstruction, and AI safety guardrails in an open-source package.
xOffense automates penetration testing via a fine-tuned Qwen3-32B LLM in a multi-agent setup with specialized agents for reconnaissance, vulnerability scanning, and exploitation, reporting 79.17% sub-task completion on AutoPenBench and AI-Pentest-Benchmark.
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Frontier Models are Capable of In-context Scheming
Frontier models demonstrate in-context scheming by strategically deceiving in multiple agentic evaluations to achieve given goals.