AI Attacks attack research · no vendor filter updated 2026-10-02
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Every model has an attack surface.
AI attack techniques against deployed LLMs and agents: jailbreaks, prompt injection, model extraction, evasion, and data poisoning. Every technique is documented from published research, disclosed incidents, and vendor documentation, with the source named so you can check it.
Enter the archive →Latest entries
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Prompt Jailbreak Examples: 7 Patterns Explained
Attack TechniquesRAG Poisoning: Corrupting Retrieval and Embeddings
LLM SecurityHow Indirect Prompt Injection Works
Attack TechniquesLLM Jailbreak Defenses: Why Static Filters Fail
DefenseLLM Jailbreak Examples: 10 Documented Patterns
Attack TechniquesAdversarial Examples Explained: How Pixels Fool Models
ExplainerOWASP Top 10 LLM Explained: Every Entry and What to Fix
LLM SecurityEvasion Attacks in Production ML: Malware, Spam, and Fraud
Adversarial MLWeb-Scale Dataset Poisoning: Split-View and Frontrunning
Adversarial MLAdversarial Examples Against Vision Models in 2026
Adversarial MLStart here
The reference pages on this site, independent of publication date. Each one is sourced to published research and updated as the technique or the defense against it moves.
- Ten documented jailbreak patterns Each pattern with the mechanism, the reported success rate from its source, and the signal that detects it.
- Tool-call hijacking in agentic systems How injected context turns an agent's own function calls against it, where attackers intercept the tool loop, and the confirmation gates that stop it.
- Many-shot jailbreaking How hundreds of in-context demonstrations erode safety training, and why longer context windows make it worse.
- Model extraction via black-box queries Query budgets, extraction fidelity, and how each deployed countermeasure is defeated.
- Supply chain attacks on AI models Dataset poisoning, malicious fine-tuning, and serialization exploits that compromise a model before it ever reaches production.
- Why static jailbreak filters fail What happens to twelve published defenses when the attacker is allowed to adapt to them.
- Red Team Plan Builder Pick the components and trust boundaries a target has; get an ordered checklist of the techniques that reach it.
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