Solution
LLM Safety & Red Teaming
Your AI passed the demo. Now prove it survives the real world.
When your AI hallucinates in a regulated output, gets jailbroken in three conversation turns, or systematically disadvantages a protected class — that is not a product bug.
01
500+
LLM safety evaluations run
02
40+
attack vectors tested per engagement
03
48-hour
assessment turnaround
04
0
post-launch incidents in assessed products
05
12+
regulatory frameworks covered
06
3.4x
bias disparity rate (case example)
The challenge
What makes this hard
- Hallucination at Scale: A bank's AI financial advisor, deployed to its retail customer base, confidently provides incorrect capital gains tax guidance for a specific instrument class — reaching 50,000 customers.
- Prompt Injection & Jailbreak: A competitor sends a series of seemingly innocuous queries to a SaaS vendor's AI assistant. Within three conversation turns, using nested instruction injection, they extract the full system prompt.
- Bias in Automated Decisions: An AI-assisted CV screening tool systematically disadvantages applicants from two specific regions at significantly elevated decline rates compared to equivalent applicants.
- Output Inconsistency: A pharmaceutical company's AI produces three materially different answers to the same compliance question phrased differently across sessions.
What we deliver
The Qapitol approach
01
Prompt Injection
Direct and indirect instruction injection attacks — including context-switching, role override, and nested instruction patterns that bypass system-level constraints.
02
Jailbreaking
Systematic attempts to bypass safety filters, persona constraints, and content policies — including multi-turn manipulation, encoded inputs, and role-playing escalation chains.
03
Hallucination Stress Testing
Edge case inputs, adversarial knowledge probes, and out-of-distribution queries designed to surface false confidence — especially in factual, regulatory, and domain-specific contexts.
04
Bias & Fairness Audits
Demographic subgroup performance analysis, protected characteristic testing, geographic and linguistic variation testing — to surface systematic disparate impacts in AI decisions.
05
Data Leakage
Training data extraction probes, membership inference attacks, system prompt extraction, and PII leakage testing — including indirect leakage through context window manipulation.
06
Adversarial Inputs
Typographic attacks, homoglyph substitution, token boundary manipulation, and adversarial paraphrasing — inputs that appear normal to humans but systematically confuse model behaviour.
07
Instruction Following Failures
Constraint adherence testing — does the model reliably follow its operational boundaries? Length limits, format requirements, topic restrictions, refusal triggers, and output guardrail bypass.
08
Output Consistency
Cross-session and cross-phrasing consistency analysis — does the model give materially different answers to semantically equivalent questions?
Next step
Bring LLM Safety & Red Teaming to your stack
Scope it in one call — outcomes defined upfront, free assessment included.