AIsecurityDojo helps developers, security teams and technical decision-makers understand and secure AI agents, LLM applications, MCP deployments, coding assistants, local AI systems, identities, data and AI development workflows.
AI systems can now read files, call APIs, browse the web, execute code, connect to tools, retrieve private information and take actions on behalf of users. That creates security risks that go beyond traditional application security. AIsecurityDojo focuses on the practical controls, testing methods and security tools needed to reduce those risks.
Explore AI Security
AI Security
Learn how to protect AI agents and LLM applications against prompt injection, data leakage, unsafe tool use, excessive permissions, poisoned context, insecure outputs and other AI-specific threats. This section includes dedicated coverage of AI Agent Security, LLM Security, Prompt Injection and RAG Security.
MCP & Developer Security
Secure the technologies developers use to connect AI to real systems. Coverage includes MCP servers and clients, Claude Code, Cursor, GitHub Copilot, Gemini CLI, OpenAI Codex, LangChain, LangGraph, CrewAI, n8n, Ollama, LM Studio and AI model supply-chain security.
Identity & Data Protection
Protect the identities, credentials and information that AI systems depend on. Learn about IAM for AI agents, OAuth, JWT, API keys, secrets management, encryption, DLP, privacy and PII protection.
Red Teaming & AppSec
Test AI systems before attackers do. Explore AI red teaming, penetration testing, vulnerability assessment, prompt-injection testing, RAG testing and secure AI development practices for authorized environments.
Security Tools
Compare products used to protect AI applications and development workflows. Our tool coverage includes best-tool roundups, detailed reviews, comparisons and alternatives across agent security, MCP, LLM protection, red teaming, identity, secrets, privacy and AppSec.
Governance
Build the organizational controls needed to manage AI safely at scale. Learn about AI governance, risk assessments, threat modeling, NIST, OWASP, MITRE ATLAS, incident response, vendor risk and security policies.
Start With the Security Problem You Need to Solve
Prompt Injection
Understand direct and indirect prompt injection, system prompt leakage, jailbreaks, malicious external content, guardrails, detection methods and practical prevention strategies.
MCP Security
Learn how to secure Model Context Protocol servers, clients and tools against tool poisoning, prompt injection, credential exposure, excessive permissions, command injection and data exfiltration.
Secrets Management
Protect OpenAI, Anthropic, Gemini and other API credentials from exposure in source code, Git repositories, logs, environment variables, browser applications and containers.
Local AI Security
Run Ollama, LM Studio and self-hosted models more safely by understanding authentication, network exposure, Docker security, local storage, model provenance and privacy tradeoffs.
AI Red Teaming
Learn how security teams test AI agents, LLMs and RAG applications for prompt injection, jailbreaks, unsafe tool use, privilege problems, data leakage and adversarial behavior.
Security Tools and Product Research
AIsecurityDojo also evaluates security products relevant to AI builders and security teams. Reviews and comparisons focus on documented capabilities, deployment model, integrations, access controls, monitoring, security coverage, limitations, pricing when available, and suitability for the specific use case.
Some product links may be affiliate or referral links. Commercial relationships do not determine our technical conclusions or prevent us from discussing limitations and alternatives. See our Affiliate Disclosure and Security Tool Review Methodology.
How We Approach AI Security Content
AI security changes quickly. Models, APIs, frameworks, MCP implementations, product capabilities and attack techniques can change after an article is published. We prioritize primary documentation, security advisories, specifications, standards, recognized security organizations and reputable technical research whenever practical.
Our goal is to distinguish documented behavior from assumptions, established risks from speculation, and defensive guidance from unsupported claims. Security testing described on AIsecurityDojo is intended for systems you own or are explicitly authorized to test.