Challenge AI behavior under realistic adversarial pressure.
Scenario-driven adversarial testing that evaluates how AI systems respond to misuse, manipulation, data exposure, unsafe actions, and control bypass attempts.
What is AI Red Teaming?
Scenario-driven adversarial testing that evaluates how AI systems respond to misuse, manipulation, data exposure, unsafe actions, and control bypass attempts.
Hackstack Security delivers it as a scoped, agentic engagement: AI agents on local and client-approved API models expand coverage, our specialists test the logic and attack chains agents miss, and every finding is validated by a human, explained in business terms, and retested after you fix it.
- AI agents plus manual testers
- Human-validated findings only
- Retest and closure status included
What outcomes should you expect?
Every engagement is designed around the risk your team needs to understand and the action it needs to take.
Evidence of control effectiveness
Prioritized AI misuse risks
Improved safety and security guardrails
What does AI red teaming testing cover?
Proven methodology plus the judgment needed to uncover context-specific risk.
Threat-led AI misuse scenarios
Jailbreak and guardrail bypass testing
Agent and tool abuse validation
Cross-user and cross-tenant data exposure testing
Safety control effectiveness review
AI red-team findings workshop
When should you run AI red teaming testing?
Test before a finding turns into a release delay, a customer escalation, or an incident.
Before a major launch, migration, or architecture change
When enterprise customers or partners request security evidence
After meaningful changes to identity, data, integrations, or infrastructure
When your team needs an independent view of real-world risk
Which questions do we test?
Frameworks support consistent coverage. The assessment remains driven by your architecture, trust boundaries, threat model, and business workflows.
- Can realistic misuse scenarios bypass policy or safety controls?
- Can encoded, multilingual, multi-turn, or indirect inputs change outcomes?
- Can an agent be manipulated into unsafe tool selection or parameter use?
- Do monitoring and escalation controls identify high-risk AI behavior?
- Scenario library mapped to system capabilities and misuse objectives
- Successful and unsuccessful bypass observations
- Control-gap analysis covering prevention, detection, and response
How does Hackstack Security test?
Testing is scoped, evidence-led, and designed to reduce risk without creating unnecessary disruption.
Context first
We learn the system, users, trust boundaries, and business objectives before testing begins.
Expert-led testing
Automation supports coverage. Human analysis finds the issues that depend on judgment and context.
Clear prioritization
Findings are ranked by exploitability, impact, and what matters to your organization.
Remediation partnership
We stay engaged through fixes, answer engineering questions, and validate closure.
What will you receive?
Reports are written for the people who need to make decisions, fix issues, and demonstrate improvement.
Executive summary and risk narrative
Detailed technical findings
Risk rating and prioritization
Exploit evidence and reproduction steps
Developer-friendly remediation guidance
Retest status and closure validation
AI Red Teaming questions, answered.
Still unsure? Ask us directly and we will reply with a proposed scope.
What does AI Red Teaming include?
It covers threat-led AI misuse scenarios, jailbreak and guardrail bypass testing, agent and tool abuse validation, cross-user and cross-tenant data exposure testing, safety control effectiveness review, and AI red-team findings workshop. Each engagement is scoped to your environment and ends with validated findings, remediation guidance, and a readout.
What will we receive at the end?
You receive an executive summary, detailed technical findings with exploit evidence and reproduction steps, risk ratings, developer-ready remediation guidance, and retest status. The goal is evidence of control effectiveness, prioritized AI misuse risks, improved safety and security guardrails.
Which frameworks guide the testing?
Coverage is aligned with OWASP Top 10 for LLM and GenAI Applications, NIST AI Risk Management Framework, MITRE ATLAS, then shaped by your architecture, trust boundaries, and business workflows.
Do you use AI agents or only manual testing?
Both. AI agents running on self-hosted local models, and on API-based models when you approve them, handle reconnaissance, surface mapping, fuzzing, and hypothesis generation, while our specialists test business logic and chained attacks manually. Every result is validated by a practitioner before it is reported.
Can you support remediation and retesting?
Yes. We walk your engineers through findings, answer implementation questions, and retest agreed fixes to document final status for stakeholders and auditors.
Ready to strengthen your AI red teaming?
Share your targets, deadline, and any compliance driver. We reply with a proposed scope, test approach, and timeline. There is no obligation.