A guide to AI models specifically designed or fine-tuned for cybersecurity tasks including vulnerability detection (VulnLLM, Foundation-Sec), safety classifiers (Llama Guard, ShieldGemma), and CTI models (ATTACK-BERT, CyberSecQwen).

How dual-objective optimization simultaneously improves LLM safety and capability, resolving the traditional safety-utility tradeoff.

The landmark DecodingTrust benchmark that won NeurIPS 2023 Outstanding Paper Award and NSA's Best Scientific Cybersecurity Paper Award for its comprehensive evaluation of GPT model trustworthiness.

How PATROL provides provable defense guarantees against adversarial policies in competitive multi-agent environments.

A comprehensive guide to privacy-preserving AI technologies including differential privacy, homomorphic encryption, secure multiparty computation, and confidential computing frameworks for protecting sensitive data in AI systems.

Exploring how peer-preservation mechanisms in frontier AI models ensure consistent behavior and prevent capability degradation across deployment contexts.

How generative model-inversion attacks can reconstruct recognizable training samples from deep neural networks, posing serious privacy threats.

How poisoned instruction-following data can backdoor language models into generating malicious outputs while appearing normal during evaluation.

How poisoning the memory and knowledge bases of LLM agents can lead to devastating adversarial attacks on agentic AI systems.

A deep dive into the ICML 2026 paper by Xu Li et al. that exposes how multi-turn conversations dramatically amplify safety risks in tool-using AI agents, and the defense strategies proposed to mitigate these escalating threats.

Computing data Shapley values in a single training run, making practical data valuation feasible for large-scale machine learning.

How to scale out-of-distribution detection from controlled benchmarks to the complexity and diversity of real-world AI deployments.

How differential privacy mechanisms can serve double duty as both privacy protectors and effective backdoor attack detectors in machine learning.

A benchmark for evaluating the safety risks of AI code agents when executing and generating potentially dangerous code.

Why the current AI data economy is unsustainable and how fair data deals can ensure long-term viability for both AI developers and data generators.

A comprehensive overview of AI-powered offensive security tools including PentestGPT, HackingBuddyGPT, CAI, Shannon, and MCP-based security testing servers that are transforming penetration testing and red teaming.

An exploration of specialized security skills for AI agents including SAST scanning skills, pentesting subagents, threat modeling capabilities, and bug bounty automation tools for Claude Code and other AI assistants.

How RigorLLM builds resilient guardrails that maintain their effectiveness against adversarial attempts to bypass content safety filters.

The discovery that naturally occurring data — not crafted perturbations — can severely degrade AI model performance, challenging the entire paradigm of adversarial robustness.

A comprehensive analysis of SteeringSafety (ICML 2026), the first systematic benchmark for evaluating representation steering attacks and defenses in large language models across multiple safety perspectives, threat models, and model families.

A comprehensive guide to the best AI security learning resources including OWASP guidelines, NIST frameworks, hands-on labs, CTF challenges, and top podcasts for security professionals.

A comprehensive safety benchmark grounded in real-world regulations and policies, ensuring AI models are evaluated against actual compliance requirements.

How incorporating self-supervised learning into training pipelines produces models that are both more robust to distribution shifts and better calibrated.

How pretraining transformers on large corpora significantly improves their robustness to out-of-distribution inputs across NLP tasks.