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Microsoft Develops Scanner to Detect Backdoors in Open-Weight Large Language Models The Hacker News
Risky shadow AI use remains widespread Cybersecurity Dive
Written quickly as part of the Inkhaven Residency . At a high level, research feedback I give to more junior research collaborators often can fall into one of three categories: Doing quick sanity chec…
Google Partner Tenex Raises $250 Million for AI Security Services Bloomberg.com
arXiv:2510.16219v3 Announce Type: replace Abstract: Malicious agents pose significant threats to the reliability and decision-making capabilities of Multi-Agent Systems (MAS) powered by Large Language…
arXiv:2508.18649v2 Announce Type: replace Abstract: Safeguarding vision-language models (VLMs) is a critical challenge, as existing methods often suffer from over-defense, which harms utility, or rely…
arXiv:2507.05660v2 Announce Type: replace Abstract: Customizing Large Language Models (LLMs) on untrusted datasets poses severe risks of injecting toxic behaviors. In this work, we introduce Optimus, …
arXiv:2505.02344v3 Announce Type: replace Abstract: The rise of LLMs has increased concerns over source tracing and copyright protection for AIGC, highlighting the need for advanced detection technolo…
arXiv:2401.01374v2 Announce Type: replace Abstract: An attack taxonomy offers a consistent and structured classification scheme to systematically understand, identify, and classify cybersecurity threa…
arXiv:2604.01831v1 Announce Type: cross Abstract: Secure long-distance communication in quantum key distribution (QKD) networks depends on trusted repeater nodes along the entire transmission path. Co…
arXiv:2604.01483v1 Announce Type: cross Abstract: The rapid evolution of autonomous, agentic artificial intelligence within financial services has introduced an existential architectural crisis: large…
arXiv:2604.01350v1 Announce Type: cross Abstract: LLM-based agents increasingly operate across repeated sessions, maintaining task states to ensure continuity. In many deployments, a single agent serv…
arXiv:2604.01330v1 Announce Type: cross Abstract: While deepfake speech detectors built on large self-supervised learning (SSL) models achieve high accuracy, employing standard ensemble fusion to furt…
arXiv:2604.02299v1 Announce Type: new Abstract: Modern adversarial campaigns unfold as sequences of behavioural phases - Reconnaissance, Lateral Movement, Intrusion, and Exfiltration - each often indi…
arXiv:2604.02149v1 Announce Type: new Abstract: As TLS 1.3 encryption limits traditional Deep Packet Inspection (DPI), the security community has pivoted to Euclidean Transformer-based classifiers (e.…
arXiv:2604.02023v1 Announce Type: new Abstract: Autonomous agents are moving beyond simple retrieval tasks to become economic actors that invoke APIs, sequence workflows, and make real-time decisions.…
arXiv:2604.01977v1 Announce Type: new Abstract: Security teams face a challenge: the volume of newly disclosed Common Vulnerabilities and Exposures (CVEs) far exceeds the capacity to manually develop …
arXiv:2604.01937v1 Announce Type: new Abstract: The UK Cyber Security and Resilience (CS&R) Bill represents the most significant reform of UK cyber legislation since the Network and Information System…
arXiv:2604.01905v1 Announce Type: new Abstract: The model context protocol (MCP) standardizes how LLMs connect to external tools and data sources, enabling faster integration but introducing new attac…
arXiv:2604.01904v1 Announce Type: new Abstract: Data rights owners can detect unauthorized data use in large language model (LLM) training by querying with proprietary samples. Often, superior perform…
arXiv:2604.01876v1 Announce Type: new Abstract: While QKD ensures information-theoretic security at the link level, real-world deployments depend on trusted repeaters, creating potential vulnerabiliti…
arXiv:2604.01750v1 Announce Type: new Abstract: Spiking Neural Networks (SNNs) are energy-efficient and biologically plausible, ideal for embedded and security-critical systems, yet their adversarial …
arXiv:2604.01645v1 Announce Type: new Abstract: Java applications are prone to vulnerabilities stemming from the insecure use of security-sensitive APIs, such as file operations enabling path traversa…
arXiv:2604.01637v1 Announce Type: new Abstract: Existing benchmarks for LLM-based vulnerability detection compress model performance into a single metric, which fails to reflect the distinct prioritie…