Topic

LLM Application Security

Securing generative AI applications end to end: model interface, retrieval, context assembly, connected tools and downstream consumers of output.

Current clusters

build1 distinct publisher

A RAG pipeline injected itself: no attacker, just a book about LLMs in the index

A developer's noise filter and reranker fixed a retrieval bug and exposed a worse one. If ordinary retrieved text can hijack a prompt, injection is a property of your corpus, not your threat model.

Publishers:dev.to

Reality

Evidence42
Adoption10
Hype gap+18
Incentives28
Confidence52

build1 distinct publisher

Your model's output leaks the prompt behind it, so stop filing system prompts under secrets

Researchers at IIT Bombay and Adobe Research train an inverse model that predicts previous tokens, recovering prompts from output text alone. It works even on outputs from models it never saw.

Publishers:dev.to

Reality

Evidence32
Adoption: Insufficient
Hype gap

security1 distinct publisher

OWASP keeps prompt injection at number one and starts managing the blast radius

The 2026 GenAI LLM Top 10 leaves the first two entries untouched and promotes Excessive Agency three places. The list increasingly reads as guidance for containing damage rather than preventing it.

Publishers:blog.checkpoint.com

Reality

Evidence38
Adoption34

Top News

Current clusters

A RAG pipeline injected itself: no attacker, just a book about LLMs in the index

Your model's output leaks the prompt behind it, so stop filing system prompts under secrets

OWASP keeps prompt injection at number one and starts managing the blast radius