AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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1,407 papers
It is shown that the same feature-learning dynamics that make neural networks powerful can also make them more vulnerable to backdoors, and that security audits based on linear heuristics can systematically underestimate backdoor vulnerability in the widely adopted feature-learning regimes.
AI-driven cybersecurity in the software engineering field is discussed, where machine learning, deep learning, natural language processing, and reinforcement learning can be applied throughout the software development lifecycle to provide increased security.
Survivors' experiences are synthesized into a framework conceptualizing how doxxing operates in mainland China, suggesting that effective protection requires constraining systemic vulnerabilities rather than relying solely on user empowerment and proposing multifaceted recommendations spanning legal reform, platform de...
The Trojan Hippo Bench is introduced, a dynamic evaluation framework for persistent memory attacks and memory-layer defenses, comprising an OpenEvolve-based adaptive red-teaming benchmark that stress-tests defenses and memory backends against continuously refined attacks, and a capability-aware security-utility analysi...
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A four-dimensional taxonomy spanning defensive function, GAN architecture, cybersecurity domain, and adversarial threat model is introduced, emphasizing hybrid models, unified evaluation, real-world integration, and defenses against emerging threats such as LLM-driven cyberattacks.
Meta-SecAlign is proposed for utility-preserving defense by (1) randomized injection position during training to avoid shortcut learning and (2) self-generated responses as high-quality in-distribution training labels as high-quality in-distribution training labels.
A novel threat is unveiled in which attackers steer the RAG system's response by injecting malicious passages into its knowledge base, enabling the attacker to steer the response without altering the user input or modifying the RAG weights.
JUMP is proposed, an efficient MIA that exploits the ability of dLLMs to predict masked tokens in parallel that improves mean ROC-AUC over a prior multi-mask attack and is extended to the target-only setting by replacing target-reference scoring with relative token preference.
Dagger, a novel two-phase decoupling-based attack framework that consistently outperforms state-of-the-art GNN stealing attacks, achieving up to 18.16\% higher fidelity while only utilizing 12.23$\times$ fewer queries than the strongest baseline.
This paper presents the design, formal analysis, and implementation of a complete blockchain-based electronic invoice system on Ethereum, and formalizes the invoice lifecycle as a guarded labeled transition system and proves that the system guarantees reimbursement uniqueness, face integrity, and authorization soundnes...
MIRAGE is proposed, a topology-concealment framework that preserves the genuine communication topology for task execution while shaping adversary-facing semantic evidence toward a carefully constructed phantom topology.
This work proposes a controlled inject-and-remove cycle: deliberately inject a weaker backdoor and then unlearn it, which weakens detector-visible signatures and fools the backdoor detectors with an illusion of purification while preserving the malicious retrieval behavior.
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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