Pentest · red teaming · AI security · automation
AI-Adversary helps engineering and security teams validate business-critical systems through manual penetration testing, controlled adversarial testing, architecture review, AI system testing, and automation-first security validation.
Core services
Engagements are designed to produce evidence that stands up to engineering review: attack paths, control gaps, architectural risks, automation opportunities, and remediation priorities.
Pentest
Application, API, cloud-facing, and identity testing led by manual expertise and supported by modern AI-assisted security tooling.
Red Teaming
Realistic offensive scenarios focused on business-critical exposure, identity abuse, cloud paths, and detection gaps.
Architecture
Technical review of systems, trust boundaries, authentication flows, cloud design, and controls before design risk becomes operational risk.
AI Testing
Adversarial testing for LLM-enabled applications, AI workflows, prompt surfaces, tool integrations, retrieval, and data exposure risks.
AI Automation
Support for teams that want to add automated security checks using AI-assisted tooling, guided by confirmed risks and manual review.
Automation
Repeatable security checks and automation pipelines that make security validation faster, more consistent, and easier to maintain.
AI-Adversary app
AI-Adversary brings a unified dashboard, API, and CLI to authorized security engagements, target management, approvals, and assessment results.
Cybersecurity education
Most serious security failures are not isolated bugs. They appear when identity, application logic, cloud configuration, data access, monitoring, and human workflows interact in unexpected ways.
A vulnerability is a weakness. Risk is the realistic impact if that weakness is used in your environment. Testing should explain both: what can be exploited and why it matters to the business.
An attack path is the sequence that turns small weaknesses into meaningful exposure, such as initial access, privilege escalation, data access, and persistence. Mapping paths helps teams prioritize what actually reduces risk.
Controls are only useful if they work under pressure. Validation checks whether authentication, authorization, segmentation, logging, alerting, and response processes behave as expected during realistic abuse.
Architecture review looks at trust boundaries, data flows, identity assumptions, and failure modes before implementation details hide the bigger design risks.
AI security is not only prompt testing. Real exposure often sits around the model: retrieval permissions, tool invocation, tenant isolation, logging, human approval, and data handling.
When a finding is fixed, it should not silently return. High-value findings can become repeatable checks that run during development, release review, or ongoing assurance.
Evidence-based delivery
The output is built for teams that need practical assurance, not vague risk language. Findings connect technical evidence to attacker objectives, affected assets, control behavior, and remediation decisions.
Choosing the right assessment
Choosing the wrong engagement type creates weak signal. A focused penetration test is useful when you need vulnerability and exploit validation. Red teaming is useful when you need to understand how far a credible attacker can get. Architecture review is useful when design decisions shape long-term risk. Automation is useful when important checks should keep running after the engagement ends.
Penetration testing
Pentest work is grounded in human judgment: understanding the system, choosing meaningful attack paths, validating exploitability, and explaining impact. Modern AI-assisted tools can help with request analysis, test generation, payload variation, and documentation review, but findings are manually reproduced and risk-ranked before delivery.
approach: manual_pentest
support: ai-assisted tooling
[tested] authz + business logic
[tested] API and cloud-facing paths
[validated] reproducible evidence
next: fix, retest, automate checksAI security focus
AI security work covers the full application context: prompts, retrieval, tools, agents, permissions, data flows, evaluation logic, and the traditional web application controls around them.
assessment: ai-security
surface: llm_app + tools + data + tests
[finding] tool invocation boundary bypass
[finding] sensitive context exposure
[validated] authz control on retrieval layer
output: evidence, exploit path, remediationMethodology
Each engagement is scoped for impact, executed with controlled testing, and delivered with findings that engineering and leadership can act on.
Clarify assets, business risks, constraints, success criteria, and safe testing boundaries.
Identify trust boundaries, exposed surfaces, likely attacker goals, and validation hypotheses.
Run technical tests, collect evidence, validate controls, and avoid unnecessary disruption.
Prioritize findings, explain impact, and provide actionable recommendations for engineering teams.
Technical insights
Detailed methods for AI security, penetration testing, architecture, and repeatable control validation.
An analysis of GHSA-9fq7-9w8p-c3qh, where missing account-type binding let a customer remembered-2FA token satisfy an administrator login.
Read articleA step-by-step method for modeling LLM application assets, trust boundaries, attacker capabilities, abuse paths, controls, and validation evidence.
Read articleStart with a focused review
Share the system, product, or AI workflow you want tested. The first step is a short scoping discussion to define objectives, constraints, and the right engagement model.