One evidence model for web, code, and AI security.

Pentoma Web

Target

Running web applications and APIs

Method

AI-assisted exploration, business-logic probing, deterministic replay, and human validation

Output

Confirmed application findings with request/response proof and compliance mappings

Pentoma Code

Target

Source-code repositories, pull requests, and release branches

Method

Offensive static analysis: vulnerability hunting, source-to-sink reasoning, attack-chain reconstruction, validated by reviewers

Output

Validated vulnerabilities, attack-chain evidence, line-level remediation guidance, and 0-day candidates worth coordinated disclosure

Pentoma AI Red Teaming

Target

LLM endpoints, RAG flows, tools, plugins, and agents

Method

Adversarial prompts, agent-boundary tests, behavior replay, and policy checks

Output

AI risk evidence mapped to OWASP LLM Top 10 and governance controls

Find with AI

Agentic discovery hunts web apps, APIs, source code, and AI systems for exploitable vulnerabilities and attack chains faster than a traditional engagement can start.

Prove with replay

Findings are promoted only when reproducible request/response, source path, or prompt evidence supports them under safe conditions.

Validate with humans

An expert reviewer checks scope, exploitability, severity, and remediation quality before a finding leaves the system.

Deliver as evidence

Every assessment ships with executive summary, engineering detail, remediation, retest status, and compliance mapping.