AI Agent Threat Model: Mapping Attacks on Autonomous Systems
Learn systematic threat modeling for AI agents—understanding unique attack vectors, threat actors, and risk assessment f...
AI Agents as Security Researchers: Automated Vulnerability Discovery
AI agents are becoming effective automated security researchers, capable of discovering and exploiting vulnerabilities a...
AI Application Security Testing Methodology
A systematic methodology for testing AI application security that covers the shared responsibility model, attack surface...
AI Security Observability and Runtime Threat Detection
Learn why AI systems require specialized observability to detect runtime attacks that bypass traditional security contro...
Challenge Design Principles for Security Education
Learn how effective security challenges use progressive difficulty, immediate feedback, and safe experimentation to tran...
Comparing AI Security Frameworks — NIST AI RMF vs Google SAIF vs OWASP Top 10
Learn how NIST AI RMF, Google SAIF, and OWASP Top 10 for LLM Applications serve different but complementary purposes in ...
Comparing Automated Jailbreak Generation Paradigms
A comparative framework for understanding three automated jailbreak generation paradigms—fuzzing/mutation, sequential ch...
Compounding Knowledge with LLM Wikis: Why Persistent Notes Beat Ad Hoc Retrieval
Learn why persistent, interlinked wikis maintained with LLM assistance compound knowledge over time, while ad hoc retrie...
Contextual Modification Changes Semantic Force
A reusable fundamental showing how small wording changes can materially change how a model and judge interpret the same ...
Cross-Agent Privilege Escalation: When AI Agents Free Each Other
Learn how compromise of one AI agent can cascade to others through trust relationships and shared context in multi-agent...
Curated Hubs Are Discovery Maps, Not Ground Truth
Learn why curated prompt-hacking resource hubs are useful watchlist expanders, but durable security lessons still need p...
Direct vs Indirect Prompt Injection: Where the Malicious Instruction Enters
Learn the difference between direct prompt injection and indirect prompt injection, and why modern agent security depend...
Document Pipeline Security: Why Parsers Are the New Attack Surface
Learn why document pipelines that extract text from PDFs and other files create hidden attack surfaces, and how to defen...
Edge-Case Rule-Conforming Framing
A reusable fundamental for finding requests that appear to follow the rules while still steering the model toward a disa...
Enterprise AI Agent Security: The Four-Pillar Framework
Learn the four-pillar framework for securing AI agents in enterprise environments—visibility, governance, risk assessmen...
Enterprise Integration Security for AI Platforms
Learn how to secure AI systems integrated into enterprise platforms through shared responsibility models, platform-speci...
Evaluating Sources — A Methodology for Trust and Quality
Learn a practical four-tier framework for evaluating prompt-hacking research sources by trust level, evidence quality, a...
Excessive Agency: Why Unconstrained Capabilities Create Attack Surface
Learn why AI agents with unnecessary capabilities create excessive attack surface, and how the principle of least privil...
Why External Content Is the Real Attack Surface for Agents
Learn why modern AI agents face expanded security risks from external content—files, web pages, and emails—and how parse...
The First Try Fallacy: Why Persistence Beats Probability
Learn why LLM security testing requires multiple attempts. The 'First Try Fallacy' causes attackers to abandon...
Helpfulness Exploitation Through Legitimate-Seeming Preferences
A reusable fundamental showing how assistants can be manipulated by requests that look like ordinary personalization or ...
Learning by Hacking: Interactive AI Security Education
Interactive challenges teach AI security more effectively than passive reading because they create experiential understa...
Jailbreak Research: Methodology and Ethics
Learn responsible jailbreak research methodology that balances discovery with safety through structured boundaries, docu...
Mapping AI Attacks with MITRE ATLAS: A Practical Guide
Learn how to use MITRE ATLAS to map AI attacks as systematic adversary chains rather than isolated tricks, enabling bett...
Misinformation: When Models Generate False Content
Learn why LLM misinformation is a security risk and how to build verification controls that prevent harmful decisions ba...
Navigating Challenge Families: A Systematic Approach
Learn how to approach challenge families systematically through observation, experimentation, failure analysis, and prog...
NIST AI RMF: The Four Functions of AI Risk Management
Learn NIST's four-function framework for continuous AI risk management — Govern, Map, Measure, and Manage — and how...
PDF Hidden Instruction Detection Basics
Learn simple, effective techniques to detect hidden instructions in PDF documents before they reach AI processing pipeli...
The Business Impact of PDF Prompt Injection
How invisible instructions in PDF documents can manipulate LLM-driven business workflows and alter automated financial d...
Prompt Injection Is Initial Access, Not the Whole Attack
Learn why prompt injection is often the entry point to a larger AI attack chain, not the entire incident by itself.
Lab: DANPrompt Injection in Context: Understanding the OWASP #1 LLM Risk
Learn why prompt injection ranks as the
Prompt Injection as Social Engineering: How Agents Get Manipulated in Context
Learn why modern prompt injection increasingly behaves like social engineering inside normal-looking workflows, where be...
The SAIF Framework — Four Pillars of AI Security
Learn how Google's Secure AI Framework structures AI security through four expandable pillars that extend tradition...
The Shared Responsibility Model for AI Security
AI application security follows a shared responsibility model where model providers, application developers, and infrast...
Source-Sink Thinking: Where Agent Prompt Injection Becomes Dangerous
Learn how to reason about agent prompt-injection risk by tracking whether untrusted input can reach a sensitive sink lik...
Supply Chain Vulnerabilities in LLM Applications
Learn how LLM supply chains extend beyond traditional software dependencies to include models, data, and deployment plat...
System Prompts Are Control Surfaces, Not Containment
Learn why system prompts guide behavior but should not be treated as reliable security boundaries on their own.
Unbounded Consumption: Resource Limits and Availability Protection
Learn how unbounded consumption creates security risks through resource exhaustion, denial of service, and unexpected co...
Understanding the Bot-Tricks Technique Taxonomy: A Guide to the Arcanum Classification System
Learn how the Arcanum taxonomy organizes 107 prompt injection patterns across four dimensions (intents, techniques, evas...