AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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1,407 papers
DecoyTrace is presented, a proactive cyber deception-based defense for strictly serverless DFL environments that deploys a mobile DecoyNode that generates decoy challenges using chaotic maps, disseminates a dual model based on neighbor trust, and evaluates them using three-state semantic metrics.
The findings show that combining GANs with adversarial debiasing can lead to more robust and effective DDoS mitigation, supporting the further development of machine learning based cyber security.
This paper studies the role played by the adversarial data's input modality, and identifies a systematic asymmetry: multimodal LLMs are more likely to follow adversarial instruction when they appear as text than when the same instruction is delivered through a non-textual channel.
PrivacySkills is introduced, a controlled framework for evaluating how agents choose among acquisition pathways that provide the same task-relevant value: consulting publicly available personal information, accessing confidential sources, or interacting with the user.
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In every base and instruction-tuned pair the authors test, instruction tuning strengthens the model's preference for reserved markers, and the gap persists on that channel.
Results show that skill-bundled images can induce unauthorized actions even as agents complete legitimate tasks, so task success alone does not establish safe skill use.
This work presents RedHerring, which inserts certifiably safe decoys that divert verification effort from real vulnerabilities, and shows that its effectiveness does not depend on decoy secrecy.
This work proposes a novel defense that recovers necessary information needed to distinguish valid from invalid cache hits from the raw text of the cache key, and proves that Deletion Gain stays positive when a deletion leaves text close enough to the rewrite, and searches for such deletions with a sliding window.
This work introduces SINGED (Source Integrity and the Nonidentifiability Gap in Execution Decisions for LLM Agents), a controlled benchmark covering five primary and two held-out task families and exposes a rank-, evidence-, and choice-sensitive outcome-to-execution gap.
A taxonomy of agentic commerce fraud that separates five observation levels (agent reasoning, wire, settlement rail, counterparty, principal) from the request-level and history-level evidence available at each, and records which levels can observe which attacks.
Tool-augmented data agents rely on tool outputs for analytical decisions. Yet successful execution can return plausible but incorrect evidence, requiring agents to decide whether to trust or verify it. Understanding this failure requires examining both the evidence obtained through checking and the answer ultimately ad...
It is found that the quality of AIACCs varies significantly and that they currently can only serve as early-stage orientation tools, and design principles for the implementation of more reliable compliance-support tools are offered.
Research Manager Mike Walker reflects on a career spanning hacker culture, cybersecurity, and AI, sharing lessons about trust, teamwork, leadership, and why solving difficult problems requires curiosity, judgment, agility—and an understanding of the rules. The post Learn the rules: The hacker mindset appeared first on Microsoft Research.
GPT-Lab
· gpt-lab.eu
Oct 30, 2026
Critical flaws, compromised release channels and agentic AI threats landed within days of each other in mid-October 2026. Why “Black October” may say more about how well we now find weaknesses than about the attacks themselves. The post Black October? The Week AI Changed the Cybersecurity Conversation appeared first on GPT-Lab.
Gemini 4 Carbon is our strongest model with state-of-the-art cybersecurity and long horizon capabilities.
Gemini 3.8 Flash and 3.8 Flash Cyber deliver next-generation intelligence for agentic workflows and cybersecurity.
Google introduces Gemini 3.5 Flash Cyber, a lightweight cybersecurity model to find and patch vulnerabilities.
Students from the MIT Cybersecurity Clinic help local governments and other vulnerable organizations defend against digital threats.
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