GenAI Security
AI moves fast, and so do its attackers. We red-team your LLM applications, secure retrieval and agent pipelines, and validate the guardrails that keep your models within bounds.
Schedule AssessmentGenerative AI expands the attack surface faster than most teams can defend it. We red-team your LLM applications, secure retrieval and agent pipelines, and validate the guardrails that keep models within bounds as they and their prompts evolve.
Engagements align with OWASP LLM Top 10 and MITRE ATLAS, chaining real exploits to show business impact and leaving a prioritized roadmap for guardrail rollout, monitoring, and continuous red-teaming rather than a one-time snapshot.
Because models and prompts change continuously, we structure the engagement for repeatable coverage: a living test suite, regression checks, and a cadence that keeps your guardrails honest as your AI estate grows.
Engagement Methodology
A structured lifecycle that maps your AI stack, attacks it like a real adversary, and leaves hardened guardrails behind, aligned with OWASP LLM Top 10 and MITRE ATLAS.
Threat Modeling & Scoping
We map your GenAI stack: model endpoints, retrieval pipelines, agents, plugins, and data flows. Together we define use cases, trust boundaries, and the assets that matter most before a single prompt is sent, so testing targets real risk rather than hypothetical noise.
- ATLAS-Recon
- LLM01
- Scoping
Adversarial Red-Teaming
Hands-on attacks against your LLM applications: direct and indirect prompt injection, jailbreaks, prompt extraction, and output manipulation. We chain real exploits end-to-end to show business impact, not just isolated theoretical weaknesses.
- LLM01
- LLM06
- LLM08
Model & Pipeline Review
We review model provenance, weight integrity, fine-tuning data, and RAG controls. Insecure serialization, poisoned datasets, and supply-chain risks in model artifacts are identified, ranked by reachability and impact, and tied to concrete mitigation.
- LLM02
- LLM03
- LLM07
Guardrail Validation
We pressure-test input/output filters, safety policies, rate limits, and access controls against OWASP LLM Top 10 and MITRE ATLAS, measuring how well your guardrails resist evasion and enforced behavior, with quantitative pass/fail rates per policy.
- LLM07
- ATLAS
- Guardrails
Reporting & Roadmap
You receive a prioritized report with reproducible proof-of-concepts, severity ratings, and a concrete remediation roadmap covering guardrail rollout, monitoring, and continuous red-teaming that scales with your AI roadmap.
- LLM10
- AI RMF
- Roadmap
Attack Surface Coverage
From prompt injection to insecure model weights, we cover the full LLM threat landscape across interaction, retrieval, models, and autonomous agents.
Prompt & Interaction
- Direct injection
- Indirect injection
- Jailbreak
- System prompt extraction
- Output manipulation
- Multi-turn bypass
Data & RAG
- Retrieval poisoning
- Sensitive leakage
- RAG access bypass
- PII exposure
- Vector DB abuse
- Cross-tenant data
Model & Supply Chain
- Insecure weights
- Model poisoning
- Fine-tuning integrity
- License & provenance
- Serialization
- Pickle RCE
Agents & Tools
- Excessive agency
- Plugin abuse
- Unauthorized actions
- Tool hijacking
- Unbounded execution
- Credential theft
Deliverables & Standards
Reproducible proof-of-concepts and a continuous red-teaming plan aligned with emerging AI security standards.
Standards & Frameworks
- OWASP LLM Top 10
- MITRE ATLAS
- NIST AI RMF
- ISO 42001
- EU AI Act
- PCI DSS
- Executive summary with AI risk posture and business-impact findings
- Technical report with reproducible PoCs for each LLM weakness
- OWASP LLM Top 10 and MITRE ATLAS mapping with severity ratings
- Guardrail and model-supply-chain review findings
- Quantitative guardrail pass/fail rates per safety policy
- Prioritized remediation roadmap for guardrails and monitoring
- Continuous red-teaming plan and regression test suite
Who This Is For
Target Profile
Who This Engagement Is For
GenAI security serves teams shipping LLM features, copilots, or agents where a prompt-injection or data-leakage failure reaches customers directly, and entities aligning with emerging AI regulations.
AI Product Teams
Teams shipping LLM features, copilots, or agents where a prompt-injection or data-leakage failure reaches customers directly.
RAG & Enterprise Search
Organizations grounding models on internal data, where retrieval poisoning and access-control bypass drive the real risk.
Agentic Workflows
Platforms deploying autonomous agents with tool access that need bounds, monitoring, and fail-safe validation.
Regulated AI
Entities aligning with NIST AI RMF, ISO 42001, or the EU AI Act that require evidence of adversarial testing.
Secure Your AI Stack
Let our team red-team your GenAI applications and harden the guardrails.
Contact Us