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PROMPT INJECTION & JAILBREAK DEFENSE
Stop instructions hidden in content from hijacking your AI.
Prompt injection is the top risk in the OWASP LLM Top 10 for a reason: any system that reads untrusted text — an email, a document, a web page, a support ticket — can have attacker instructions smuggled in alongside the legitimate content. We test for it and build the layers that catch it.
ATTACK PATTERN→ DEFENSE
Five places AI risk actually lives.
Traditional application security tooling doesn't see any of these by default. Each one needs its own review, its own controls, and its own owner.
ATTACK PATTERN
HOW IT WORKS
DEFENSE LAYER
Direct injection
A user types instructions directly aimed at overriding the system prompt.
System-prompt hardening and instruction-priority enforcement.
Indirect injection
Instructions are hidden inside a document, email, or web page the AI is asked to read.
Content sanitization and source-trust boundaries before content reaches the model.
Jailbreaking
Roleplay or hypothetical framing used to talk the model past its own guidelines.
Layered refusal testing across common jailbreak patterns.
Output manipulation
Injected instructions try to make the model produce harmful or unauthorized output downstream.
Output validation before any action or response is executed.
Multi-turn escalation
An attack is built gradually across several turns to avoid single-message detection.
Conversation-level monitoring, not just single-message filtering.
HOW AN ENGAGEMENT RUNS
Three stages, from testing to hardened deployment.
01
Red-team the current system
We run a structured set of injection and jailbreak attempts against your AI-facing system to find what currently gets through.
02
Harden the layers
System prompt structure, input sanitization, and output validation are strengthened based on what the red-team exercise found.
03
Re-test and monitor
The same attack set is re-run to confirm the fix, and ongoing monitoring is put in place for new patterns as they emerge.