Test AI systems before users and attackers do.
Expert-led security testing for AI-enabled applications, LLM workflows, copilots, agents, retrieval systems, and the APIs that connect them.
What is AI & LLM Security Testing?
Expert-led security testing for AI-enabled applications, LLM workflows, copilots, agents, retrieval systems, and the APIs that connect them.
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.
Validated AI application risks
Safer model and tool integration
Practical AI security controls
What does AI & LLM security testing cover?
Proven methodology plus the judgment needed to uncover context-specific risk.
Prompt injection and instruction hierarchy testing
Sensitive data and system prompt exposure review
RAG and vector-store access control testing
Tool-use and agent authorization review
Model abuse and unsafe output analysis
AI application threat modeling
When should you run AI & LLM security 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 direct or indirect prompt injection alter intended behavior?
- Can users retrieve system prompts, sensitive context, or another tenant’s data?
- Can model output reach downstream interpreters without safe validation?
- Can tools, agents, or workflows act beyond the requesting user’s authority?
- Adversarial prompt and retrieval test cases
- Data-flow and trust-boundary findings for models, tools, and stores
- Guardrail effectiveness observations with repeatable scenarios
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 & LLM Security questions, answered.
Still unsure? Ask us directly and we will reply with a proposed scope.
What does AI & LLM Security Testing include?
It covers prompt injection and instruction hierarchy testing, sensitive data and system prompt exposure review, RAG and vector-store access control testing, tool-use and agent authorization review, model abuse and unsafe output analysis, and AI application threat modeling. 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 validated AI application risks, safer model and tool integration, practical AI security controls.
Which frameworks guide the testing?
Coverage is aligned with OWASP Top 10 for LLM and GenAI Applications, NIST AI Risk Management Framework, NIST Generative AI Profile, 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.
What if the test finds no High or Critical vulnerabilities?
This assessment is eligible for our guarantee: if the final report contains no validated High or Critical finding, you are not charged the audit fee, subject to the guarantee terms agreed in your statement of work.
Ready to strengthen your AI & LLM security?
Share your targets, deadline, and any compliance driver. We reply with a proposed scope, test approach, and timeline. There is no obligation.