01 / LLM
LLM Penetration Testing
Adversarial testing of model-integrated applications and their surrounding controls.
- Prompt injection
- System prompt leakage
- Unsafe output handling
- Abuse-case testing
AI Security
Secure AI before
attackers break it.
Push AI to Its Breaking Point.
Uncover vulnerabilities across LLMs, RAG pipelines, agentic systems, and data boundaries before attackers do.
AI attack surface
Security beyond
the chat interface.
Modern AI products combine models, prompts, retrieval, tools, identity, data and conventional application infrastructure. Each layer changes the threat model.
02 / RED TEAM
AI Red Teaming
Scenario-led attempts to bypass protections and create real business impact.
- Goal hijacking
- Guardrail evasion
- Data extraction
- Multi-step attack chains
03 / AGENTS
Agent Security
Review the permissions, tools, memory and actions that make autonomous systems powerful.
- Tool misuse
- Excessive agency
- Identity and access
- Action validation
04 / RAG
RAG Security
Protect retrieval pipelines from poisoned content, boundary failures and sensitive data exposure.
- Knowledge-base poisoning
- Retrieval manipulation
- Tenant isolation
- Source trust
05 / SUPPLY CHAIN
Model & Pipeline Security
Assess dependencies, model provenance, data flows and the infrastructure around inference.
- Model supply chain
- Training-data protection
- Secrets and logging
- Deployment review
The WRYVY difference
Build. Break.
Govern.
One continuous assurance path connects how the AI system is engineered, how it fails under pressure, and how the evidence improves the next release.
Understand the system
Architecture, retrieval, orchestration, agents, evaluation and production MLOps shape the assessment.
- Model and provider integration
- RAG and vector architecture
- Agent tools and permissions
Challenge the assumptions
Adversarial scenarios reveal where instructions, trust boundaries and controls fail under pressure.
- Manual prompt and workflow attacks
- Cross-layer attack chains
- Business-impact validation
Make risk operable
Convert findings into engineering action, ownership and evidence for leadership and governance.
- Prioritised remediation
- Secure design recommendations
- AI risk roadmap
Continuous AI assuranceSystem context → Adversarial evidence → Governed changeRelease with confidence ↗
Assessment path
From architecture
to adversarial proof.
Scope adapts to the system, but the engagement follows a clear decision path.
01⎔
Map system
Trace models, data, retrieval, tools and trust boundaries.
02△
Model threats
Define abuse cases, failure modes and likely adversaries.
03✣
Design attacks
Build scenarios across prompts, RAG, agents and data.
04ϟ
Execute
Challenge live controls with manual adversarial testing.
05◎
Validate impact
Prove technical consequence and business relevance.
06✦
Remediate
Prioritise controls and practical engineering changes.
07✓
Retest
Confirm that fixes reduce the intended risk.
System-aware testingCross-layer evidenceAdversarial proof ↗
AI security outcomes
Know where the
system can fail
—before launch.
The assessment should answer concrete questions for product, engineering, security and governance teams.
01
Attack paths
Which inputs and workflows can be manipulated?
02
Data boundaries
Can users reach information they should not see?
03
Agent authority
Can model-driven actions exceed intended permissions?
04
Control evidence
What needs to change, who owns it, and how is it verified?
Before the next release