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The Summer 2026 AI Safety Index Turns Voluntary Pledges Into a Scorecard

The Future of Life Institute's Summer 2026 AI Safety Index says frontier AI firms weakened pledges as capabilities kept advancing.

OpenAI GPT-5.6 Sol Turns Reasoning Models Into Long-Running Cyber and Work Agents

OpenAI GPT-5.6 Sol brings stronger cyber, science, coding, and tool-use performance to paid ChatGPT and agent workflows.

Anthropic Mythos at the NSA Puts Frontier Cyber Models Inside the Hardest Governance Test

Reported NSA Mythos work shows frontier cyber models are moving from gated research into sensitive government operations.

Anthropic's AI Cyber Threat Map Shows Agentic Security Is No Longer Optional

Anthropic's AI-enabled cyber threat research maps autonomous attack chains, MITRE ATT&CK gaps, and why Agentic AI needs stronger security controls.

Anthropic Mythos Access for ENISA Tests a New Model Release Pattern

Anthropic's reported Mythos expansion to ENISA and vetted security teams turns frontier cyber models into controlled-defense infrastructure.

Meta AI Support Bot Shows Why Account Recovery Is Too Sensitive for Loose Agents

Reported Instagram account takeovers through Meta's AI support flow expose the risks of giving agents account recovery permissions.

Enterprise AI Agents Are Becoming Users Before Security Teams Are Ready

Agentic AI is forcing companies to redesign identity, access control, monitoring, and governance around non-human users.

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.

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.

GPT-5.5-Cyber and Trusted Access: How Verified Defenders Are Reshaping the Cyber Market

OpenAI's expansion of Trusted Access for Cyber with GPT-5.5 and GPT-5.5-Cyber shows how verified access, safety controls, and defender tooling are redefining the cyber market.

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.

Running Codex safely at OpenAI

OpenAI's latest Codex safety framing shows how sandboxing, approvals, network policies, and telemetry are turning coding agents into production systems.

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.