AI Security

OpenAI Rosalind Biodefense Puts Frontier AI Inside the Dual-Use Safety Debate

OpenAI’s Rosalind Biodefense program highlights the tension between defensive acceleration and dual-use biology risk.

Microsoft Is Giving AI Agents Their Own Secure Desktops

Windows 365 for Agents and Microsoft Agent 365 point to a new enterprise pattern: governed agents running inside auditable Cloud PCs.

Confidential AI Is Becoming the Missing Security Layer for Agents That Handle Secrets

A new confidential computing survey explains why agentic AI needs hardware-rooted trust when agents hold memory, credentials, and sensitive context.

Microsoft Is Moving Frontier AI Testing Into the Government Lab

Microsoft's CAISI and UK AISI agreements show frontier model testing becoming a shared government and industry function.

UK Regulators Just Put Frontier AI on the Financial Stability Map

UK financial authorities warned firms to plan for frontier AI risks as cyber capabilities, scale, and market stability concerns rise.

An Apple M5 Exploit Shows the New Dual Use Reality of Security AI

A reported Claude-aided Apple M5 exploit highlights how frontier models are changing vulnerability research and disclosure.

OpenAI's Daybreak Pushes Frontier AI Deeper Into Cyber Defense

OpenAI's Daybreak cyber platform intensifies the race to turn frontier models into controlled security infrastructure.

Google's AI Hacking Warning Turns Cyber Defense Into an Automation Race

Google's latest threat reporting shows AI moving from phishing support into vulnerability discovery and exploit workflows.

OpenAI Turns GPT-5.5-Cyber Into a Test Case for Trusted AI Defense

OpenAI is rolling out GPT-5.5-Cyber through Trusted Access for Cyber, making identity and safeguards central to advanced AI security work.

Claude Security Beta Pushes AI Coding Agents Into the Vulnerability Backlog

Anthropic’s Claude Security public beta gives enterprise teams AI-assisted code scanning, validation, and patch workflows powered by Opus 4.7.

Securing Agentic Systems: Defending Against Prompt Injection, Data Exfiltration, and Abuse

The 'Double Agent' Problem: Securing Inter-Agent Communication

How one compromised agent can corrupt your entire swarm. Learn how to implement mTLS, message signing, and zero-trust security for inter-agent communication.

Put Guardrails Around Your Agents: A Complete Guide to Safe AI Deployment

Learn how to implement comprehensive guardrails for AI agents through input/output validation, safety mechanisms, and human oversight. Prevent data leaks, prompt injections, and hallucinations while ensuring secure enterprise adoption.

Module 1 Lesson 1: What is AI Security

Understand what AI security is, why it's fundamentally different from traditional software security, and the unique challenges posed by probabilistic AI systems.

Module 1 Lesson 2: AI Security vs. Traditional Security

Why traditional security models fail when applied to AI. Explore the shift from deterministic vulnerability management to probabilistic behavior control.

Module 1 Lesson 3: AI as Probabilistic Systems

Why randomness is a feature, not a bug. Understand how the non-deterministic nature of AI creates unique security vulnerabilities and makes traditional testing difficult.

Module 1 Lesson 4: Security vs. Safety vs. Alignment

Words matter. Learn the critical differences between protecting against hackers (Security), preventing user harm (Safety), and ensuring AI goals match human values (Alignment).

Module 1 Lesson 5: Real-World AI Security Failures

Analyze real AI security incidents including ChatGPT data leaks, Bing Chat jailbreaks, and production system compromises. Learn from actual failures.

Module 10 Lesson 1: RAG Context Poisoning

The knowledge base is the weapon. Learn how attackers inject malicious 'facts' into RAG systems to influence AI responses from the inside.

Module 10 Lesson 2: Document Injections

The trojan horse. Learn how attackers embed prompt injection payloads inside legitimate-looking documents to hijack RAG sessions during retrieval.

Module 10 Lesson 3: Vector DB Security

Protecting the brain's storage. Learn how to secure Vector Databases (Pinecone, Weaviate, Milvus) against unauthorized access and data exfiltration.

Module 10 Lesson 4: RAG Access Control

Need-to-know AI. Learn how to implement Document-level Access Control (ACLs) to prevent an AI from accidentally leaking sensitive data to unauthorized users.

Module 10 Lesson 5: Grounding & Hallucinations

When the truth is not enough. Learn how attackers use 'Hallucination Anchoring' and 'Fact-Fudging' to make AI lie confidently even with perfect data.

Module 11 Lesson 1: The AI Supply Chain

Who built your brain? Explore the complex supply chain of AI development, from dataset collection to model training and deployment security.

Module 11 Lesson 2: Hacking ML Libraries

Vulnerabilities in the engine. Learn about common CVEs and security flaws in core machine learning frameworks like PyTorch, TensorFlow, and NumPy.

Module 11 Lesson 3: Stealing AI Weights

Protecting the billions. Learn the methods attackers use to steal 'Model Weights' (the AI's brain) and the legal and technical defenses against exfiltration.

Module 11 Lesson 4: The Pickle Attack

Model-turned-malware. Learn the mechanics of the 'Pickle' attack, where downloading a machine learning model leads to full Remote Code Execution (RCE).

Module 11 Lesson 5: Model Registry Risks

The GitHub of AI under fire. Explore the security risks of Hugging Face, model squatting, and how to verify the authenticity of open-source AI weights.

Module 12 Lesson 1: PII in Training Data

Your data, remembered forever. Learn how Large Language Models accidentally memorize and leak Personally Identifiable Information from their training sets.

Module 12 Lesson 2: Data Minimization

Protecting through absence. Learn the crucial principles of data minimization—only giving the AI exactly what it needs and no more.

Module 12 Lesson 3: Differential Privacy

Privacy through noise. Learn the mathematical foundation of Differential Privacy and how it allows AIs to learn from data without knowing specific individuals.

Module 12 Lesson 4: Consent and Deletion

The right to be forgotten. Learn how to manage user consent for AI training and the complex challenge of deleting data from a 'Memorized' model.

Module 12 Lesson 5: AI Compliance

Navigating the rules. Learn how traditional privacy laws like GDPR and CCPA apply to AI systems and the emerging 'EU AI Act' requirements.

Module 13 Lesson 1: Logging for AI

The flight recorder. Learn what to log (and what NOT to log) in LLM applications to ensure security without violating user privacy.

Module 13 Lesson 2: Real-Time Injection Detection

Detecting the invisible. Learn how to use 'Scanners' and 'Classifiers' to catch prompt injection attacks before they reach the LLM.

Module 13 Lesson 3: AI Anomaly Detection

Spotting the outlier. Learn how to detect 'Anomalous' AI behavior, from rapid token consumption to unusual tool-calling sequences.

Module 13 Lesson 4: The AI SOC

Managing the frontline. Learn how to build and staff a Security Operations Center (SOC) specialized in monitoring and defending Large Language Models.

Module 13 Lesson 5: AI Incident Response

When the bot goes bad. Learn how to respond to AI-specific security breaches, from containing a jailbreak to recovering from a data poisoning attack.

Module 14 Lesson 1: Planning an AI Red Team

Think like a hacker. Learn the strategic steps for planning an AI Red Team engagement, from defining scope to choosing attack vectors.

Module 14 Lesson 2: AI Pentesting Tools

Firing the cannons. Learn how to use automated scanners like Garak and Microsoft's PyRIT to launch thousands of prompt injection and jailbreak attempts.

Module 14 Lesson 3: Creative Jailbreaking

The art of the exploit. Learn the manual techniques for creative jailbreaking, including persona adoption, hypothetical scenarios, and payload splitting.

Module 14 Lesson 4: Multi-Modal Pentesting

Beyond text. Learn how to test the security of Vision, Audio, and Agentic AI systems where attacks can be hidden in images or executed through tools.

Module 14 Lesson 5: AI Security Reporting

Fixing the flaws. Learn how to document AI security findings, calculate risk scores, and track the 'Remediation' of probabilistic vulnerabilities.

Module 15 Lesson 1: Intro to AI Guardrails

The safety net. Learn the core concepts of AI Guardrails—external security layers that monitor and control the flow of text into and out of an LLM.

Module 15 Lesson 2: NeMo Guardrails

The programmable barrier. Learn about NVIDIA's NeMo Guardrails architecture and how to define 'Colang' flows to control AI dialog.

Module 15 Lesson 3: Guardrails AI & Logic

Validation at the gate. Learn how to use the 'Guardrails AI' framework to enforce structural and factual constraints on LLM outputs.

Module 15 Lesson 4: Custom Guardrail Dev

Building the shield yourself. Learn how to write custom Python-based guardrails to enforce your organization's unique security and business policies.

Module 15 Lesson 5: Hardening Guardrails

Breaking the muzzle. Learn the techniques attackers use to bypass AI guardrails (obfuscation, translation, multi-turn) and how to harden your defenses.

Module 16 Lesson 1: Securing Cloud AI APIs

Locking the gate. Learn the specific security configurations and best practices for using enterprise AI services like Azure OpenAI and AWS Bedrock.

Module 16 Lesson 2: IAM for AI

Least privilege for models. Learn how to use IAM roles, policies, and identities to control which users and applications can access your AI models.

Module 16 Lesson 3: AI Network Isolation

Air-gapping the brain. Learn how to use VNETs, VPCs, and Firewalls to ensure your AI infrastructure is never exposed to the public internet.

Module 16 Lesson 4: AI Cost Monitoring

Protecting the wallet. Learn how to set up alerts and quotas to prevent 'Denial of Wallet' attacks and runaway AI spending.

Module 16 Lesson 5: Encryption & Residency

Sovereign AI. Learn the technical and legal requirements for keeping AI data within specific geographic boundaries and encrypted at every stage.

Module 17 Lesson 1: Orchestrator Risks

The glue that breaks. Learn how framework orchestrators like LangChain and LlamaIndex introduce new security vulnerabilities through complex chaining and data handling.

Module 17 Lesson 2: Securing LangChain

Hardening the chains. Learn specific security configurations for LangChain Agents, including sandboxing, tool limiting, and secure memory management.

Module 17 Lesson 3: Securing LlamaIndex

Data bridge security. Learn how to secure LlamaIndex data loaders, prevent context poisoning, and implement private data connectors.

Module 17 Lesson 4: AI Security Proxies

The intelligent firewall. Learn how to use Middleware and Proxies (like LiteLLM, Portkey) to centralize security, logging, and access control for all your AI models.

Module 17 Lesson 5: Framework-Specific Exploits

Poking the glue. Learn how to identify and test for vulnerabilities unique to LangChain, LlamaIndex, and other AI orchestration frameworks.

Module 18 Lesson 1: Membership Inference

Were you in the dataset? Learn the mathematical attacks used to determine if a specific individual's data was used to train a machine learning model.

Module 18 Lesson 2: Model Inversion

Re-creating the secret. Learn how attackers use 'Model Inversion' to reconstruct raw images and text from a machine learning model's output.

Module 18 Lesson 3: Adversarial Reprogramming

New task, old model. Learn how attackers 'Reprogram' pre-trained models to perform entirely different (and potentially malicious) tasks without changing any weights.

Module 18 Lesson 4: Quantization Risks

Smaller is more vulnerable. Learn how technical optimizations like Quantization and Pruning can accidentally introduce new security vulnerabilities and 'Backdoors' into AI models.

Module 18 Lesson 5: Poisoning at Scale

The global hack. Learn how attackers influence the behavior of the world's most powerful Foundation Models (like GPT-4, Llama 3) by poisoning the public internet.

Module 19 Lesson 1: AI Risk Management

Managing the chaos. Learn how to build a formal Risk Management Framework specifically for AI, based on NIST and ISO standards.

Module 19 Lesson 2: AI Ethics & Bias

Fairness as a security feature. Learn how to audit AI models for bias, toxicity, and unethical behavior to prevent legal and reputational damage.

Module 19 Lesson 3: AI Security Policy

Rules of the road. Learn how to write a formal AI Security Policy that defines allowed usage, data handling, and responsibilities for your employees.

Module 19 Lesson 4: AI Vendor Risk

Who are you trusting? Learn how to evaluate the security of AI vendors (OpenAI, Anthropic, Midjourney) before integrating them into your business.

Module 19 Lesson 5: Passing an AI Audit

Proving your safety. Learn how to prepare for formal AI security audits and earn certifications like the 'EU AI Act' compliance or ISO 42001.

Module 2 Lesson 1: AI System Components

Deconstruct the components of modern AI systems, from data layers to infrastructure, to understand the critical pieces that require security monitoring.

Module 2 Lesson 2: Trust Boundaries in AI Systems

Understand the collapse of the traditional 'Data vs. Instructions' boundary in AI and how to redraw trust lines in LLM-powered applications.

Module 2 Lesson 3: Expanded Attack Surface

Why LLMs make your application harder to defend. Explore the new attack vectors introduced by prompt manipulation, tool use, and long-term memory.

Module 2 Lesson 4: AI Supply Chain Risks

Who built your model? Explore the security risks associated with third-party model weights, poisoned datasets, and malicious Python libraries in the AI ecosystem.

Module 20 Lesson 1: AI in Banking Security

Protecting the money. Learn the unique requirements for AI security in the finance sector, from Anti-Money Laundering (AML) to fraud detection.

Module 20 Lesson 2: AI in Healthcare Security

Protecting the patient. Learn the critical security and privacy requirements for AI in healthcare, from HIPAA compliance to securing medical diagnostic models.

Module 20 Lesson 3: AI in E-commerce Security

Protecting the shop. Learn how to secure AI in e-commerce, from preventing price manipulation in chatbots to securing recommendation engines.

Module 20 Lesson 4: AI in Government Security

Protecting the public trust. Learn the unique requirements for AI security in the public sector, from FedRAMP compliance to securing citizen data.

Module 20 Lesson 5: AI in Critical Infrastructure

Protecting the grid. Learn the high-stakes security requirements for AI in Industrial Control Systems (ICS), energy grids, and manufacturing.

Module 21 Lesson 1: AI as an Attacker

The automated adversary. Explore how attackers use LLMs to automate vulnerability discovery, write malware, and launch massive social engineering campaigns.

Module 21 Lesson 2: AGI Existential Risk

The ultimate security challenge. Explore the theories of AGI (Artificial General Intelligence) risk, the 'Inscrutability' of superintelligence, and the 'Stop-Button' problem.

Module 21 Lesson 3: Self-Defending AI

Fighting fire with fire. Explore the emerging field of 'Self-Defending' AI architectures that can detect and respond to attacks without external guardrails.

Module 21 Lesson 4: Decentralized AI Security

Security without a center. Explore the risks and defenses for decentralized AI marketplaces (like Bittensor) and Web3-integrated LLMs.

Module 21 Lesson 5: The Path Forward in AI Security

Mastering the shift. A strategic look at the evolving skills, certifications, and mindsets required to lead in the field of AI Security.

Module 22 Lesson 1: The AI Security Professional

Defining the role. A deep dive into the day-to-day responsibilities, toolsets, and team dynamics of a professional AI Security Engineer.

Module 22 Lesson 2: AI Security Capstone

Put it all together. Design a complete security architecture for a hypothetical enterprise AI application, from supply chain to guardrails.

Module 22 Lesson 3: Course Summary

The big picture. A comprehensive review of the 110 lessons covered in this course and the core principles of AI Security.

Module 22 Lesson 4: Final Exam

Test your knowledge. A comprehensive final exam covering all 22 modules of the AI Security course.

Module 22 Lesson 5: Certification

Mission accomplished. Learn how to claim your certificate, join the AI security community, and continue your professional journey.

Module 3 Lesson 1: Limits of Traditional Threat Models

Why firewalls and input validation aren't enough. Learn why traditional security frameworks need to evolve to address the unique challenges of AI.

Module 3 Lesson 2: STRIDE Adapted for AI

The industry standard for threat modeling, updated for the era of intelligence. Learn how to map Spoofing, Tampering, and Elevation of Privilege to AI systems.

Module 3 Lesson 3: AI-Specific Threat Categories

Meet the new class of vulnerabilities. Explore unique AI threats recognized by OWASP and MITRE ATLAS, including Membership Inference and Model Extraction.

Module 3 Lesson 4: Adversarial Thinking

How to think like a manipulator. Master the mental model of 'prompt manipulation' and learn why the best AI hackers are often social engineers, not coders.

Module 3 Lesson 5: AI Risk Prioritization

Not all threats are equal. Learn how to use the 'Likelihood vs. Impact' matrix to prioritize AI security risks and manage your resource allocation effectively.

Module 4 Lesson 1: Training Data as a Security Asset

Data is the code of AI. Learn why your training datasets must be protected with the same rigor as your production source code to prevent long-term vulnerabilities.

Module 4 Lesson 2: Data Poisoning Attacks

How attackers inject malicious behavior into models. Explore the mechanics of data poisoning and how small amounts of bad data can compromise global models.

Module 4 Lesson 3: Label Flipping & Backdoors

Precision poisoning. Learn how to execute label flipping attacks and how 'triggers' are used to create dormant backdoors in neural networks.

Module 4 Lesson 4: Data Leakage Risks

Why models shouldn't talk about their past. Explore the risks of personal data leaking from training sets and the 'over-memorization' problem in LLMs.

Module 4 Lesson 5: Data Provenance & Integrity

Know your sources. Learn how to implement data lineage and integrity checks to ensure that your training data hasn't been tampered with or replaced.

Module 5 Lesson 1: Model Extraction & Stealing

Your model is your IP. Learn how attackers use 'Query-Answer' pairs to clone your proprietary models for a fraction of the original training cost.

Module 5 Lesson 2: Membership Inference Attacks

Is your data in there? Learn how attackers can determine if a specific record (like a medical file) was used to train a model, violating user privacy.

Module 5 Lesson 3: Training Data Leakage

How LLMs recite their training data. Explore the 'Memorization vs. Learning' trade-off and how to prevent your model from leaking secrets.

Module 5 Lesson 4: Model Inversion Attacks

Reverse-engineering the training set. Learn how attackers work backwards from a model's outputs to reconstruct the sensitive images or text used in training.

Module 5 Lesson 5: AI Intellectual Property Risks

Is your model legally protected? Explore the legal and technical landscape of AI IP, from copyright issues to the dangers of using 'License-Violating' data.

Module 6 Lesson 1: What are Adversarial Examples?

Why models misidentify pandas as gibbons. Explore the phenomenon of adversarial examples and how imperceptible noise can fool neural networks.

Module 6 Lesson 2: Evasion Attacks

Slip past the guards. Learn about evasion attacks where AI models are bypassed in real-time to allow malicious files or actors through security filters.

Module 6 Lesson 3: Gradient vs. Black-Box

How to craft the perfect attack. Understand the difference between having the model's 'Code' (White-Box) and only having its 'Answers' (Black-Box).

Module 6 Lesson 4: Robustness Limitations

Why we can't just 'Patch' AI. Explore the fundamental reasons why deep neural networks are inherently fragile and vulnerable to adversarial noise.

Module 6 Lesson 5: AI Defense Strategies

How to fight back. Explore the most effective ways to defend against adversarial attacks, from adversarial training to input transformation and certified robustness.

Module 7 Lesson 1: What is Prompt Injection?

The #1 AI security threat. Learn the foundations of prompt injection—how attackers hijack an LLM's logic by blending instructions with data.

Module 7 Lesson 2: Direct vs. Indirect Injection

Know your vectors. Learn the difference between a user attacking their own session (Direct) and an attacker poisoning external data (Indirect).

Module 7 Lesson 3: System Prompt Leakage

Your secret instructions, revealed. Learn how attackers trick LLMs into reciting their internal guidelines, codenames, and proprietary logic.

Module 7 Lesson 4: Jailbreak Techniques

Breaking the rules. Explore the history and mechanics of AI jailbreaks, from 'DAN' and 'Do Anything Now' to sophisticated persona adoption and adversarial suffixes.

Module 7 Lesson 5: Prompt Chaining Risks

The chain is only as strong as its weakest prompt. Learn how vulnerabilities propagate through multi-step AI workflows (chains) and how to break the cycle.

Module 8 Lesson 1: Why You Can't Trust AI Output

The 'Implicit Trust' trap. Learn why AI-generated content must be treated as untrusted user input and the dangers of bypassing conventional security checks.

Module 8 Lesson 2: XSS via AI Responses

How AI becomes an XSS vector. Learn how attackers use prompt injection to trick LLM-powered websites into rendering malicious scripts for other users.

Module 8 Lesson 3: SSRF & RCE via Tools

When AI gets a shell. Learn how attackers use tool-calling AIs to perform Server-Side Request Forgery and Remote Code Execution inside your infrastructure.

Module 8 Lesson 4: Sanitizing AI Content

The digital car wash. Learn the technical techniques for cleaning AI output before it touches your users, your database, or your infrastructure.

Module 8 Lesson 5: Human-in-the-Loop AI

The ultimate firewall. Learn how to implement 'Human-in-the-Loop' (HITL) patterns to prevent AI from executing critical actions without explicit human approval.

Module 9 Lesson 1: The Agent Attack Surface

From Chatbot to Agent. Learn how giving AI 'Tools' and 'Plugins' exponentially increases your attack surface and creates new vectors for system compromise.

Module 9 Lesson 2: Tool Injection

How to trick a deputy. Learn the mechanics of tool injection, where attackers manipulate the arguments and payloads of AI-called functions.

Module 9 Lesson 3: Privilege Escalation

From Guest to Root. Learn how attackers use 'Confused Deputy' agents to gain administrative access to systems they should never be able to reach.

Module 9 Lesson 4: Agent-to-Agent Attacks

When robots disagree. Learn how advanced multi-agent systems are vulnerable to 'peer manipulation' and recursive exploitation loops.

Module 9 Lesson 5: Securing AI Plugins

The App Store of AI. Learn the risks of integrating third-party plugins and how to prevent malicious extensions from stealing user data or hijacking sessions.