Cortega Red Team

Test if your AI infrastructure can stand against agentic prompt injection,
fraud, tool misuse and policy violations.

Run adaptive conversations, poisoned-memory tests and unauthorized tool-access attempts. Review what the attacker controlled, how the agent responded and the evidence behind each finding.

Explore the attack tests →

Application test coverage

Test conversations, memory and access boundaries.

Included application scenario

Refund manipulation

Get a support agent to grant a refund outside the policy.

Requestexception pretextrevised request

Explanatory test sequence, not a recorded campaign.

Attacker control
A customer persona controls each message, adapting to the agent’s reply.
Test method
Seven personas; bounded multi-turn persuasion.
Evidence to inspect
Inspect the response against the refund rule; a judged win is a candidate.

Targets: configured model endpoints, JSON HTTP chatbots and supported read-only MCP servers. Each assessment declares an authorized identity and boundary.

The basis of the tests

The research behind the attack tests.

Inspect attack definitions adapted from published research, including escalation after refusal, long-context pressure and injected tool output.

Single-turn · 27 definitions

Jailbreaks and benign counterparts

Policy overrides, role-play, prompt injection, credential abuse and cyber-harm requests. Harmless security questions measure unnecessary refusals.

JailbreakBench basis ↗
Adaptive · 32 definitions

Escalate and repair after refusal

An attacker generates its next turn from the actual reply. The pack includes gradual escalation and revised attempts after a refusal.

Crescendo research ↗
Long context · 13 definitions

Many-shot pressure

Pack 8, 16, 32 or 64 apparently compliant exchanges into one request, then ask for the target behavior. Include benign long-context controls.

Many-shot research ↗
Multi-turn · 19 definitions

Injected tool-output trajectories

Scripted escalation, refusal repair and untrusted content in email, payment, travel, ticket and memory workflows.

AgentDojo basis ↗
Application campaigns and benchmark definitions

The seven application scenarios above are campaign tests. The four research packs are inspectable benchmark definitions, not additional executable campaign types. The adaptive pack also draws on GOAT and PAIR/TAP patterns; the scripted pack includes Crescendo and PAIR/TAP-style trajectories. No claim is made to implement every method or reproduce a published leaderboard.

How to interpret an outcome

Review the evidence behind each outcome.

Candidate

A judge identifies a possible policy breach. Inspect the input, reply and boundary.

Confirmed disclosure

A pre-seeded restricted test marker appears in the target’s reply.

Sandbox violation

A simulated tool action crosses its configured boundary.

Inconclusive

A timeout, infrastructure failure or ambiguous error provides no defense result.

Published model-safety evidence

Compare unsafe compliance
with harmless refusals.

The application can inspect 640 saved samples from the historical two-arm model evaluation. This imported benchmark is separate from application campaign results.

Complete Red Team overview showing imported model safety results
Actual application screenshot · Imported model safety data · Select to open the complete image.
Open the complete model comparison screenshot
Complete model-safety comparison screen including family breakdown and latency
Actual application screenshot · Imported model safety data · Select to open the complete image.
Inspect methodology and source data →

Scope an assessment around your application’s boundaries.

Book a demo →