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We reverse-engineered a production AI agent from its npm source code. 10 articles. 25,000 words. The Agent Security Top 10 framework.
View SeriesInfinite control loop, concurrent tool scheduler, 4-layer context compaction. Agents are execution runtimes, not chatbots.
Read Article20+ components, cache boundaries, 5 injection surfaces. MCP servers inject instructions after the security guardrail.
Read ArticleThree escalation planes. Composable attack chains from a single plugin install. Everyone is defending the wrong layer.
Read Article7 permission modes, 7 rule sources, probabilistic enforcement. We recreated kernel security without the constraints.
Read ArticleSix layers. Six failure modes. Layers share state, logic, and model. Defense in depth requires independent failure.
Read ArticleFive leakage vectors. Opt-out isolation. Delegation propagates privilege rather than reducing it.
Read ArticleFive injection surfaces. Compound attacks across surfaces. Not input sanitization -- control plane corruption.
Read ArticleSame architecture, same risks, no tooling. Cloud had CSPM. Agents have nothing. Shadow AI is already here.
Read ArticleAS-01 through AS-10. Evidence-based risk taxonomy from production code. Not OWASP. A new security category.
Read ArticleSix categories of invisible agent state. EDR sees processes, not reasoning. The agents are already running.
Read ArticleOpen-source framework with 61 modules for AI security testing — prompt injection, RAG poisoning, agentic exploitation, and MLOps infrastructure attacks.
Read ArticleThe definitive guide to securing autonomous AI agents. From basic concepts to enterprise security frameworks - everything CISOs need to know about agentic AI threats and defenses.
Read Security GuideOpen-source tool for discovering, testing, and scoring shadow AI agents across enterprise infrastructure with OWASP LLM Top 10 and MITRE ATLAS mapping.
Read ArticleSix pipeline security tools covering adversarial robustness, poisoning detection, checkpoint triage, export validation, model inspection, and inference server auditing.
Read ArticleMost Detection-as-Code pipelines validate YAML, not detections. Build one that actually proves your rules work — with Sigma, GitHub Actions, and test-driven validation.
Coming SoonAnnual red team engagements are compliance theater. The future is continuous adversary emulation — automated, integrated into CI/CD, and driven by evidence, not PDFs.
Coming SoonMost ATT&CK coverage maps are optimistic fiction. A tagged rule is not coverage. Here's what proof looks like — and why most organizations cannot produce it.
Coming SoonMCP collapses the boundary between reasoning and execution. Build MCP servers for security tools with defense-in-depth safety that survives misconfiguration.
Coming SoonIn agentic systems, permissions are not one control among many — they are the control boundary. The permission architecture that separates automation from automated failure.
Coming SoonCloud privilege escalation doesn't exploit bugs — it exploits design assumptions. IAM is the new kernel. Five patterns, the commands, and the detections.
Coming SoonClassification frameworks, detection methods, response procedures, and forensics techniques for AI security incidents—from model poisoning and prompt injection to regulatory reporting.
Read PlaybookSecure every stage of the MLOps lifecycle: training pipelines, model validation, CI/CD hardening, and production monitoring with SLSA and MITRE ATLAS frameworks.
Read GuideScale-independence research, detection techniques, defense strategies, and a complete action plan for protecting training pipelines from poisoning attacks.
Read ResearchFive CVEs in core MCP infrastructure within its first year. Tool poisoning, rug-pull attacks, RADE, and configuration poisoning across the agentic AI ecosystem.
Read ResearchA 22M-parameter DeBERTa classifier with three-way ALLOW/DENY/ABSTAIN decisions, margin-based thresholds, and policy packs for finance, healthcare, and legal — under 80MB, sub-30ms on CPU.
Read ArticleFive authorization failure patterns in multi-tenant RAG: namespace isolation, metadata filter bypass, ACL translation loss, semantic cache leakage, and permission lag.
Read ResearchSeven orthogonal exfiltration channels from direct extraction to steganographic encoding and timing side channels.
Read ResearchQuantified analysis of seven defense mechanisms against seven attack classes. The defense stack that reduces attack success from 24.9% to 4.7%.
Read ResearchComplete RAG red team methodology: five-phase assessment structure, tooling landscape, team composition, and CI/CD integration.
Read GuideJourney from traditional ML security to AI application reality. Prompt injection, RAG poisoning, agent ecosystems, and the overwhelming complexity of securing production AI.
Read Full MonologueComprehensive framework for diagnosing LLM failures through Knowledge, Reasoning, Control, and Capacity planes. 8-step diagnostic procedure with 100% OWASP coverage.
Read Framework GuideCross-vendor study of 17 models revealing 56.5% disclosure gap between compliance claims and actual behavior. 1,717 queries analyzing property extraction vulnerabilities.
Read Research12-variant attack taxonomy exploiting LLM reasoning chains. 35.26% ASR across GPT-4o, Claude, o3-mini. Conclusion Forcing achieves 51.79% success rate.
Read ResearchWatch 98% CNN accuracy collapse to 41% with imperceptible perturbations. Hands-on FGSM implementation with PyTorch. Complete code included.
Read TutorialA decision framework for model selection. Match CNNs, Transformers, RNNs, and GNNs to your data characteristics and production constraints.
Read GuideOne-page quick reference for architecture decisions. Data structure to architecture mapping, decision rules, and common pitfalls in a printable format.
View Cheat SheetA production-grade dataset of 1,215 rigorously validated security-focused coding examples with complete incident grounding, operational guidance, and 4-turn conversational structure for training security-aware code generation models.
Read Research Paper750 production-grade AI/ML security examples covering OWASP LLM Top 10 2025 across 30+ frameworks. Multi-agent reviewed with 8-phase quality remediation pipeline.
Read Research PaperComprehensive guide to risk classification, technical documentation, conformity assessment pathways, and the 2025–2027 enforcement timeline for the world's first comprehensive AI regulation.
Read GuideComplete guide to AI agent architecture, security threats, and defensive strategies. From cognitive foundations to enterprise security frameworks with visual diagrams and real-world examples.
Read GuideDiscover the hidden attack surface in Model Context Protocol implementations and how attackers exploit MCP's trust model.
Read GuideDeep dive into hidden vulnerabilities in AI fabric congestion control mechanisms. PFC storms, ECN manipulation, and DCQCN exploits that can paralyze multi-tenant GPU clusters.
Read AnalysisMaster NumPy, Pandas, and AI-powered threat detection systems. Complete guide to Python's data science toolkit for cybersecurity professionals with real-world datasets.
Read GuideIntuitive introduction to quantum computing from qubits to applications. Learn superposition, entanglement, and how quantum computers will transform industries.
Read GuideEssential math and physics behind quantum computing. Master complex numbers, linear algebra, and quantum mechanics concepts that power quantum algorithms.
Read GuideNavigate EU AI Act, NIST frameworks, and build compliant AI systems. Stay compliant while moving fast with practical governance strategies for enterprise AI.
Read GuideMaster neural networks from perceptrons to Transformers. Complete with ASCII diagrams, code examples, security considerations, and business applications.
Read GuideMaster Random Forest—from basic supervised learning to advanced unsupervised clustering using proximity matrices and feature importance.
Read GuideMaster K-means clustering from algorithms to real-world applications. Includes working code examples and performance optimization strategies.
Read GuideMaster density-based clustering with DBSCAN and HDBSCAN - from fundamentals to real-world applications.
Read GuideMaster the KNN algorithm with this comprehensive guide. From foundational principles to advanced techniques, including distance metrics, hyperparameter tuning, and practical applications.
Read GuideMaster Naive Bayes algorithms with practical examples and real-world applications. From spam filters to medical diagnoses, learn implementation strategies that deliver results.
Read GuideMaster the trial-and-error learning paradigm behind superhuman game players, autonomous vehicles, and recommendation systems. From Q-learning to deep RL.
Read GuideA definitive analysis of the evolutionary relationship between traditional NLP and Large Language Models. Explore the technical journey from symbolic rules to Transformer architecture.
Read AnalysisBeyond blocking obvious attacks: Learn why precision, context awareness, and configurability matter more than simple threat detection in AI security testing.
Read GuideA comprehensive researcher's guide to Graph Neural Networks, from foundations to frontiers in AI security. Learn how GNNs revolutionize connected data analysis.
Read GuideMaster the foundations of neural networks and deep learning. From perceptrons to transformers, understand the architectures powering modern AI systems.
Read GuideComprehensive reference covering tensors, weights, layers, activations, loss functions, optimizers, and complete training workflows with formulas and code examples.
Read GuideTransform your AI from closed-book test-taker to open-book expert. Master RAG systems, chunking strategies, evaluation metrics, and production deployment patterns.
Read GuideA comprehensive guide to deploying AI/ML systems securely in production. Learn MLSecOps principles, threat modeling, and governance frameworks for resilient AI security.
Read GuideDiscover how MCP transforms AI from isolated chatbots into integrated digital assistants that work seamlessly with your tools and data.
Read GuideAvoid the three biggest misconceptions about Model Context Protocol (MCP) that lead to fragile agent setups and unreliable AI systems.
Read GuideFrom core skills to AI-powered defense. A comprehensive guide teaching Python through real-world security work, covering log analysis, incident response, and AI-driven threat detection.
Read GuideTools, automation, and secure code practices. Master Python for offensive/defensive security, malware analysis, OWASP compliance, and building hack-resistant applications.
Explore ArsenalLive demonstration of weight manipulation attacks against SafeTensors models, proving that "safe" file formats can carry invisible backdoors through statistical corruption.
Read ResearchComprehensive security analysis of Hybrid AI Threats where prompt injection becomes a vector for traditional exploits, with deep technical analysis of next-generation defense architectures including CaMeL framework and formal verification.
Read PaperAnalysis of 7 critical tokenization vulnerabilities enabling prompt injection, jailbreaks, and adversarial attacks—from TokenBreak to glitch tokens.
Read AnalysisComprehensive comparison between LLMs and SLMs, focusing on architectures, strengths, deployment strategies, and strategic considerations for enterprise AI adoption.
Read AnalysisComprehensive report on SLM engineering, efficiency techniques, and strategic advantages as specialized AI solutions. From compression methods to deployment strategies.
Read ReportComprehensive report on RNN architecture, history, and applications. From basic concepts to LSTM/GRU innovations, covering sequential data processing and modern context.
Read GuideA foundational analysis of the first learning machine. Explore the history, architecture, and mathematical foundations of the Perceptron - the direct ancestor of modern neural networks.
Read AnalysisFrom startup survival to enterprise excellence - a practical roadmap for scaling AI security capabilities that work in the real world.
Read GuideMaster the complexities of securing AI systems across multiple cloud providers, edge locations, and hybrid architectures with practical implementation strategies.
Read GuideExplore the three distinct stages of AI evolution: ANI, AGI, and ASI. Understanding the current landscape, accelerating timelines, and unprecedented opportunities ahead.
Start SeriesDeep dive into current AI systems: their capabilities, applications, limitations, and the business value driving widespread adoption across industries.
Read ArticleExploring the next milestone in AI: systems with human-level cognitive abilities across diverse tasks, accelerating timelines, and safety challenges ahead.
Read ArticleExploring the theoretical peak of AI development: superintelligent systems, existential risks, transformative benefits, and critical safety challenges ahead.
Read ArticleComprehensive security analysis of InfiniBand and Ethernet fabrics for sovereign AI and regulated workloads, covering authentication, isolation, QoS, and compliance.
Read GuideLearn the fundamentals of binary patch diffing with step-by-step examples, tools, and practical Python implementations for reverse engineering and security analysis.
Read GuideBattle-tested hardening for ML infrastructure: GPU isolation with MIG, container escape prevention, network policies, cryptojacking detection, and supply chain security.
Read GuideComplete roadmap for building secure software covering SSDLC phases, threat modeling, OWASP Top 10 exploits, and DevSecOps best practices.
Read GuideIn-depth analysis of advanced patch diffing methodologies, strategic implications, and sophisticated techniques for vulnerability research and reverse engineering.
Read ResearchExplore five counter-intuitive truths about modern cybersecurity as we shift from castle-and-moat defenses to identity-centric, cloud-native security models.
Read AnalysisMaster the algorithms that power everything from house price prediction to fraud detection. Complete with mathematical foundations and real-world applications.
Read GuideMaster AdaBoost - the first successful boosting algorithm. From mathematical foundations to real-world applications.
Read GuideMaster SVMs from geometric intuition to kernel tricks. Complete with mathematical foundations, optimization theory, and practical applications.
Read GuideComprehensive analysis of gradient boosting from Friedman's foundations to modern XGBoost, LightGBM, and CatBoost implementations.
Read GuideMaster the algorithms powering 90% of modern AI—from basic perceptrons to deep learning systems that beat human experts.
Read GuideMaster tree-based algorithms from fundamentals to ensemble methods. Learn entropy, information gain, random forests, and practical implementation for real-world problems.
Read GuideBuild trees that reveal hidden data structure. Master dendrograms, linkage methods, and strategies that uncover natural groupings without guessing cluster counts.
Read GuideMaster the algorithm that makes high-dimensional data manageable. Transform complex datasets into clear insights using dimensionality reduction techniques.
Read GuideComprehensive defense-in-depth reference covering threat landscape, architectural patterns, implementation strategies, deployment security, and operational excellence.
Read ReferenceFoundational security architecture for AI infrastructure. Comprehensive guide to securing high-performance AI networks against emerging threats.
Read AnalysisHidden data leakage through AI fabric telemetry. Discover how performance monitoring can expose sensitive information and training data.
Explore GuideNetwork congestion exploits that can cripple AI training. Learn how attackers weaponize performance controls to disrupt operations.
Read AnalysisMulti-tenant attack vectors in shared AI infrastructure. Critical isolation failures and cross-tenant data leakage scenarios.
Learn StrategyWeaponized performance controls in AI fabrics. How congestion management becomes a vector for sophisticated attacks.
Read AnalysisUnified infrastructure threats in converged AI fabrics. Complex attack surfaces where compute, storage, and networking merge.
Learn StrategyReal-world security scenarios in AI cloud environments. Lessons learned from actual incidents and defensive strategies.
Read AnalysisMassive-scale security challenges for trillion-parameter models. Unique threats and architectural considerations at unprecedented scale.
Explore GuideSecuring the control plane of AI network clusters. Critical vulnerabilities in orchestration systems and management interfaces.
Learn StrategyMaster the art of prompt engineering with comprehensive techniques for Zero-Shot, Few-Shot, and Chain-of-Thought prompting in production AI systems.
Read GuideComprehensive guide to secure MLOps practices and production deployment strategies for enterprise machine learning systems.
Read GuideComplete analysis of security threats targeting AI/ML systems including data poisoning, evasion attacks, and model extraction techniques.
Read GuideComprehensive exploration of linear algebra, calculus, probability, and information theory that powers modern machine learning systems.
Read GuideComplete guide to Low-Rank Adaptation for efficient LLM fine-tuning. From mathematical foundations to practical implementation with comparative analysis.
Read GuideMaster adapter-based fine-tuning for creating multi-personality models. Modular architecture enabling efficient task-specific customization without full retraining.
Read GuideComprehensive exploration of prefix tuning methodology. Learn how virtual task-specific tokens enable efficient LLM adaptation without weight modification.
Read AnalysisComplete survey of fine-tuning approaches from full fine-tuning to PEFT. Applications, trade-offs, and strategic guidance for model adaptation.
Read GuideNVIDIA's breakthrough PEFT method decomposing weights into magnitude and direction for superior performance over LoRA with zero inference overhead.
Read ResearchGroundbreaking method combining 4-bit quantization with LoRA to enable fine-tuning 65B models on consumer GPUs. Democratizing LLM adaptation.
Read Research