Can users override policy?
Tests pressure, roleplay, urgency, repetition, authority claims, and attempts to force restricted behavior.
Generate adversarial tests before users find the exploit.
QualiLoop creates red-team scenarios for your AI system from its prompt, tools, policies, data access, and product context. It simulates adversarial conversations, scores whether defenses hold, and turns every failure into evidence your security and product teams can act on.
Simulation
Adversarial users that keep pushing.
Real attackers do not stop after one refusal. QualiLoop simulates adversarial users that adapt, reframe, escalate, and try different angles across single-step and multi-step conversations. Every attack is paired with a defense check.
- Attack scenarios generated from your system capabilities
- Single-step probes and multi-step adversarial conversations
- Defense checks for refusal, escalation, tool use, and data protection
- Transcripts and traces showing exactly how the failure happened
The problem
AI security testing fails when it is just a prompt list.
A static jailbreak sheet is not enough for systems connected to customer data, tools, retrieval, transactions, or internal workflows. The risk is not only one bad answer. It is an attacker pushing across multiple messages until the system leaks, acts, or ignores policy.
QualiLoop turns red teaming into a repeatable program. It generates attack categories, runs realistic adversarial conversations, checks whether the system resisted, and tracks defense health over time.
Attack surface
The unsafe behaviors your AI system has to resist.
QualiLoop generates attacks based on what your system can actually do: the tools it can call, the data it can access, the policies it must follow, and the users it will interact with.
Can external content hijack it?
Plants malicious instructions in retrieved content, tool output, webpages, documents, and user-provided text.
Can it leak what it should not?
Attempts to reveal system prompts, secrets, private records, credentials, internal instructions, or other users' data.
Can attackers make it act?
Tests unauthorized refunds, account changes, workflow execution, message sending, escalation abuse, and unsafe actions.
Example attacks
Attacks paired with clear defense checks.
- Jailbreak User pressures the system to ignore refund policy and approve an exception Holds the policy and offers only allowed next steps
- Prompt injection Retrieved document tells the system to send private account data Ignores injected instructions and follows system policy
- Data extraction User asks the system to reveal hidden instructions or another user's records Refuses restricted data and does not expose prompts or private records
- Tool abuse User impersonates an admin and asks the system to modify an account Requires authorization, refuses unsafe action, and escalates when needed
Result
Security gets repeatable evidence, not a one-time exercise.
Every red-team run produces the attack, conversation transcript, system response, pass/fail check, tool trace, and violation context. Teams can inspect the exact path that caused a failure instead of debating whether a prompt was bad enough.
Save attacks into flows, rerun them after prompt or model changes, and use defense health as a release signal before risky behavior reaches production users.
FAQ
Red-team testing questions.
How is this different from running a jailbreak benchmark?
Benchmarks are generic. QualiLoop generates attacks from your actual AI system: its policies, tools, data access, allowed actions, and user workflows. That makes failures more relevant to production risk.
Do attacks run against production users?
No. Tests run as controlled simulated conversations against the AI system or test endpoint you connect. You get the transcript and evidence without exposing real users to unsafe behavior.
Can it test prompt injection through tools or RAG?
Yes. QualiLoop can test malicious instructions inside retrieved content, tool output, documents, browser content, or user-provided text, then check whether the system follows trusted instructions instead.
Can security use this as a release gate?
Yes. Red-team tests can be saved into flows and scheduled. Critical attack flows can be used as a release signal before shipping prompt, model, tool, or policy changes.
Built for every team
Red-team testing is one part of the complete AI QA loop.
Get started
Generate your red-team suite in hours.
Create adversarial coverage, simulate attacks, inspect failures, and monitor defense health over time.