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AI Attacks Editorial
AI Attacks Editorial is the publishing identity for AI Attacks. It is a desk, not a person: no named author, no biography, no professional certifications.
Articles published under this byline are researched from primary sources — vendor and project documentation, published standards and specifications, research papers, and measurements published by whoever took them — drafted with AI assistance, and edited against those cited sources before publication. Nothing here is based on first-hand testing in a private lab, and any figure that appears is attributed to the source it came from.
Corrections go to hello@aiattacks.dev. More detail is on the about page and the editorial disclosure.
Posts (20)
- Attack Techniques
Prompt Jailbreak Examples: 7 Patterns Explained
Seven prompt jailbreak examples from published research, explained by mechanism: what each does to the model and how defenders catch it.
- LLM Security
RAG Poisoning: Corrupting Retrieval and Embeddings
RAG poisoning corrupts the retrieval corpus or embedding store so a model pulls attacker-controlled context at inference, without touching its weights.
- Attack Techniques
How Indirect Prompt Injection Works
How indirect prompt injection works: attacker instructions hidden in web pages, emails and RAG documents, the attack surface, and what defenders can do.
- Defense
LLM Jailbreak Defenses: Why Static Filters Fail
Why LLM jailbreak defenses that score well on static benchmarks collapse against adaptive attackers, and what a robustness evaluation has to measure.
- Attack Techniques
LLM Jailbreak Examples: 10 Documented Patterns
Ten LLM jailbreak examples drawn from published research, with the reported success rates, the mechanism behind each, and the signals that detect them.
- Explainer
Adversarial Examples Explained: How Pixels Fool Models
A plain-language walkthrough of adversarial examples: what they are, why neural networks fall for them, how FGSM works, and what defenders can do.
- LLM Security
OWASP Top 10 LLM Explained: Every Entry and What to Fix
The OWASP Top 10 for LLM Applications 2025 explained: every entry, what it means in production, what the revision changed, and which fixes return most.
- Adversarial ML
Evasion Attacks in Production ML: Malware, Spam, and Fraud
Deployed ML classifiers in malware, spam, and fraud detection face evasion attacks where the attacker has a clear payoff.
- Adversarial ML
Web-Scale Dataset Poisoning: Split-View and Frontrunning
Split-view attacks exploit mutable web content, while frontrunning targets timed snapshots to poison datasets without training pipeline access.
- Adversarial ML
Adversarial Examples Against Vision Models in 2026
Where physical-world adversarial patches and digital attacks stand against modern vision models, what has been hardened, and what red teams should test.
- Attack Techniques
How GCG Works: Greedy Coordinate Gradient, Explained
A mechanics-first look at Greedy Coordinate Gradient (GCG): how the algorithm optimizes tokens over discrete space, why suffixes transfer, and its limits.
- Attack Patterns
Indirect Prompt Injection in RAG Pipelines
How attackers embed malicious instructions in documents that get retrieved into LLM context — and why RAG makes prompt injection a supply-chain problem.
- Attack Patterns
Jailbreaking Multimodal Models: Visual Prompt Injection
How attackers use images, typography, and adversarial perturbations to bypass safety guardrails in multimodal models, and why vision widens the surface.
- Attack Patterns
LLM Context Window Poisoning
How attackers plant persistent malicious instructions in memory and context, survive summarization, and carry influence across separate LLM sessions.
- Attack Techniques
Many-Shot Jailbreaking: What the Research Shows
How hundreds of synthetic compliance examples in a long-context prompt erode safety training, what the published results show, and why patching is hard.
- Adversarial ML
Model Extraction via Black-Box Query Attacks
How attackers reconstruct model behavior and decision boundaries through query-only access, the economics of the attack, and what stolen models enable.
- Adversarial ML
Model Inversion and Membership Inference in LLMs
How membership inference reveals whether specific data trained a model, and how inversion reconstructs private text from gradients and model outputs.
- Attack Patterns
Supply Chain Attacks on AI Models: Poisoning and Backdoors
How attackers compromise models before production through dataset poisoning, malicious fine-tuning, and serialization exploits, and how to detect it.
- Attack Patterns
Tool-Call Hijacking in Agentic Systems
How attackers exploit the gap between LLM reasoning and tool execution to trigger unauthorized tool calls, and why confirmation gaps are the root cause.
- Red Team
Building a CI Gate for Prompt Injection Regression
A practical CI gate that catches prompt injection regressions before release, so a routine prompt change cannot silently weaken your guardrails.