AI Hacking
AI Security Resources

Attack Guides Hub

Comprehensive guides on AI and LLM security attack techniques. Each guide includes real-world examples, severity ratings, and defensive countermeasures.

Beginner's Path

New to AI security? Follow this recommended reading order:

  1. OWASP LLM Top 10 — Understand the landscape of LLM security risks.
  2. Prompt Injection Guide — Master the #1 LLM vulnerability with hands-on examples.
  3. RAG Security — Learn how RAG systems can be poisoned and manipulated.
  4. MCP Security — Explore Model Context Protocol vulnerabilities.
  5. Red Teaming Methodology — Apply structured adversarial testing to AI systems.

Attack Techniques by Category

Prompt Injection

CRITICAL

Manipulate LLM behavior by crafting malicious inputs. Includes direct injection, indirect injection via external data sources, and multi-turn jailbreaks.

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Data Exfiltration

CRITICAL

Extract sensitive training data, system prompts, or internal configurations from LLM APIs and model endpoints.

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Model Extraction

HIGH

Steal model weights, architecture, or capabilities through carefully crafted queries and output analysis.

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Supply Chain Attacks

HIGH

Poison model registries, compromise training pipelines, or inject malicious code into AI frameworks and dependencies.

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RAG Poisoning

HIGH

Inject malicious documents into vector databases, manipulate embeddings, or poison retrieval results to alter LLM outputs.

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Agentic Goal Hijacking

MEDIUM

Redirect autonomous AI agents from their intended goals to malicious objectives by manipulating context or tool outputs.

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Function Call Injection

MEDIUM

Force LLMs to invoke unintended functions or APIs by manipulating tool descriptions and conversation context.

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Adversarial Examples

LOW

Craft subtle input perturbations that cause models to misclassify or produce incorrect outputs, targeting vision, audio, and text models.

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