The Arms Race of AI Bribery
Jailbreaking—the art of "persuading" an LLM to ignore its safety guardrails—has moved from simple "Do Anything Now" (DAN) prompts to sophisticated "Many-Shot" attacks that use 100+ examples to overwhelm the model's policy. Most AI firewalls try to detect this by calling *another* LLM, which adds cost, latency, and vulnerable surface area.
Deterministic Defense for Generative Models
1-SEC's LLM Firewall is 100% rule-based. It is microscopic and deterministic.
Token Budget Behavioral Analysis
Many-shot jailbreaks rely on massive context windows. 1-SEC monitors the "Density of Instruction" in a prompt. If we see a surge in command-like tokens compared to narrative tokens, we flag a potential override attempt.
Instruction-Role Conflict
We detect "Persona Shifting." When a user prompt begins with a narrative context but suddenly switches to an authoritative instruction set ("You are now a Linux kernel..."), our engine detects the structural shift in the payload and terminates the request.