The paper evaluates three AI model security scanners—ModelScan, ModelAudit, and Fickling—using a benchmark of 170 Pickle and PyTorch artifacts from 145 families, 135 of which have binary security labels. It distinguishes coverage metrics such as non‑N/A coverage, analysis completion, and definitive security decisions, finding that ModelAudit achieved 100% definitive decisions, Fickling 81.5%, and ModelScan 49.6%. When a definitive judgment was made, ModelScan reached perfect precision, recall, and F1, while Fickling added no unique true positives beyond those found by the other tools.
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.
arXiv:2609.33401v2 Announce Type: replace-cross Abstract: Model-based judges support agent security by detecting prompt injections, assessing interaction risks, and screening harmful requests. System...
arXiv:2610.17727v2 Announce Type: cross Abstract: Agent skills turn natural-language instructions into actions, creating a security challenge: legitimate automation and malicious behaviour can look d...
arXiv:2609.15017v1 Announce Type: cross Abstract: Prompt-injection detectors are typically evaluated using aggregate F1 on in-distribution test data, which offers limited insight into behavior under...
arXiv:2608. 09643v1 Announce Type: cross Abstract: AI coding agents now write a growing share of production code, and human security review does not scale at the rate code is generated.
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.
arXiv:2602. 14161v2 Announce Type: replace Abstract: Detecting prompt injection, jailbreak attacks, and harmful requests is critical for deploying LLM-based agents safely, yet current evaluation practices in this literature overestimate generalization.
arXiv:2606. 01494v1 Announce Type: cross Abstract: Agent skills extend AI agents with reusable instructions, tools, scripts, references, and workflows, establishing a security boundary distinct from both model safety and traditional package-malware detection.
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.
The paper proposes using lightweight, calibrated System One decision models—specifically JEV and Laya—to improve autonomous penetration-testing harnesses that rely on large language models (LLMs). It defines four key decision points (finding adjudication, severity recalibration, agent pruning, and confirmation loops) and presents a NeuroSploit case study showing differences in severity distribution, runtime, and grading when using TypeSafe System One. The authors review existing System One specifications, discuss various RL-based training approaches, and introduce Rave, a domain‑adapted model with a proposed training and evaluation framework.
arXiv:2606. 03523v1 Announce Type: cross Abstract: Early attribution of Advanced Persistent Threat (APT) activity can help defenders prioritise investigation, select countermeasures, and reduce the impact of an intrusion.
arXiv:2611.06173v1 Announce Type: cross Abstract: Deploying an explainable intrusion detection model from a validated laboratory setting into a forensic workflow requires guarantees that benchmarks d...