Pentest · red teaming · AI security · automation

Security testing for systems that need real assurance.

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.

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Attack path validation
Manual pentesting
LLM application testing
Security architecture
Automation-first delivery

Core services

Manual offensive testing, AI-assisted validation, and architecture review for modern systems.

Engagements are designed to produce evidence that stands up to engineering review: attack paths, control gaps, architectural risks, automation opportunities, and remediation priorities.

Pentest

Manual penetration testing

Application, API, cloud-facing, and identity testing led by manual expertise and supported by modern AI-assisted security tooling.

Exploit validation
Authorization and business logic testing
AI-assisted coverage under human review

Explore pentesting

Red Teaming

Controlled adversary simulation

Realistic offensive scenarios focused on business-critical exposure, identity abuse, cloud paths, and detection gaps.

External and internal attack paths
Detection and response validation
Executive-ready reporting

Explore red teaming

Architecture

Security architecture review

Technical review of systems, trust boundaries, authentication flows, cloud design, and controls before design risk becomes operational risk.

Threat modeling and design review
Cloud and identity boundaries
Remediation roadmap

Review architecture

AI Testing

AI and LLM security testing

Adversarial testing for LLM-enabled applications, AI workflows, prompt surfaces, tool integrations, retrieval, and data exposure risks.

Prompt and tool abuse scenarios
Data leakage assessment
AI workflow threat modeling

Assess AI systems

AI Automation

AI-assisted security testing automation

Support for teams that want to add automated security checks using AI-assisted tooling, guided by confirmed risks and manual review.

AI-assisted test generation
Security regression scenarios
Human-validated outputs

Add AI-assisted testing

Automation

Testing automation and validation

Repeatable security checks and automation pipelines that make security validation faster, more consistent, and easier to maintain.

Security regression checks
Custom test harnesses
CI/CD validation support

Automate testing

Cybersecurity education

Good security testing answers practical questions, not just technical checklists.

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.

Vulnerability vs. risk

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.

Attack path

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.

Control validation

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.

Secure architecture

Architecture review looks at trust boundaries, data flows, identity assumptions, and failure modes before implementation details hide the bigger design risks.

AI system security

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.

Security regression

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

Security work that explains what was tested, what held, what failed, and what to do next.

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.

Clear scope and assumptionsTesting objectives, boundaries, target assets, business risks, and known constraints are made explicit before execution.
Reproducible evidenceFindings include enough technical detail for engineering teams to understand impact, reproduce where appropriate, and validate fixes.
Risk-informed remediationRecommendations prioritize exploitable paths, exposed data, weak trust boundaries, and control gaps that matter most to the system.
Automation opportunitiesWhere repeat testing makes sense, checks can be converted into security regression tests or validation workflows.

Choosing the right assessment

Pentest, red team, architecture review, or automation: each answers a different question.

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.

Use a pentest whenYou have an application, API, cloud-exposed service, or release candidate that needs hands-on validation of exploitable issues.
Use red teaming whenYou need to test attack paths, identity abuse, detection coverage, and response readiness against realistic objectives.
Use architecture review whenYou are making design decisions around trust boundaries, identity, data flows, cloud controls, or AI-enabled workflows.
Use automation whenYou want confirmed risks, critical controls, or AI workflow scenarios to become repeatable validation checks.

Penetration testing

Manual security expertise, supported by AI-assisted tooling where it improves coverage.

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.

Penetration testing Automate confirmed findings Red team validation

approach: manual_pentest
support: ai-assisted tooling

[tested] authz + business logic
[tested] API and cloud-facing paths
[validated] reproducible evidence

next: fix, retest, automate checks

AI security focus

AI systems introduce new attack surfaces. Test them like software, workflows, and adversarial interfaces.

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.

AI security testing AI-assisted testing automation AI trust boundary review

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, remediation

Methodology

A clear process from scope to evidence.

Each engagement is scoped for impact, executed with controlled testing, and delivered with findings that engineering and leadership can act on.

01 / Scope

Define objectives

Clarify assets, business risks, constraints, success criteria, and safe testing boundaries.

02 / Model

Map attack paths

Identify trust boundaries, exposed surfaces, likely attacker goals, and validation hypotheses.

03 / Test

Execute controlled testing

Run technical tests, collect evidence, validate controls, and avoid unnecessary disruption.

04 / Improve

Deliver remediation guidance

Prioritize findings, explain impact, and provide actionable recommendations for engineering teams.

Start with a focused review

Need assurance before launch, audit, or scale?

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.

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