Validated chain
Indirect injection → tool abuse
Malicious content in RAG corpus→retrieved into prompt→instructs agent to call CRM update tool
Business impact
Customer record modification reachable via AI agent
Discover · AI / LLM Surface
Validate the new AI attack surface
Prompt injection, tool abuse, agent jailbreak, training-data exfiltration — validated against your live AI applications, with reproducible evidence.
- OWASP LLM Top 10 covered
- RAG + agent + frontier coverage
- Production-safe ramp-up
- EU AI Act / NIST AI RMF aligned
LiveAI / LLM Surface · Live
ValidatedThe problem
AI applications introduce attack surfaces traditional tools do not test
LLM-backed apps trust user input, downstream tools and retrieved context in ways traditional applications do not. The boundary between data and instruction is blurred. New attack categories — prompt injection, tool abuse, agent jailbreak — exist outside conventional pentest scope.
Most security tools cannot test them at all.
The KeenSafe approach
Adversarial validation built for AI and LLM stacks
KeenSafe runs adversarial validation against LLM-backed applications: prompt injection, indirect injection via retrieved content, tool-call abuse, agent-loop hijack, training-data exfiltration and model-replacement risk.
Reproducible. Production-safe. Mapped to OWASP LLM Top 10 and emerging AI risk frameworks.
Capabilities
What ships in this engagement
Prompt Injection
Direct + indirect (RAG context) injection chains validated against production endpoints.
Tool Abuse
Agent tool surfaces tested for unsafe execution, parameter injection and unauthorized side-effects.
Agent Loop Jailbreak
Multi-turn jailbreak validation across persistent agent contexts.
Training-Data Exfiltration
Membership inference, prompt-extraction and model-inversion validation.
Model Supply Chain
Validation of model provenance, signing, and replacement-risk paths.
OWASP LLM Mapping
Findings mapped to OWASP LLM Top 10 (LLM01–LLM10) with auditor-ready exports.
Attack path
How attackers actually move
Real AI attack paths chain prompt injection into actual business impact. Pure jailbreak is not interesting; jailbreak that triggers a privileged tool call is.
Validated chain
Prompt extraction → IP leak
Multi→turn extraction→reveals system prompt + tool descriptions→exposes proprietary workflow
Business impact
Internal IP leak via published AI assistant
Outcomes
Measurable, evidence-backed
OWASP LLM
Full coverage
LLM01–LLM10 with reproducible evidence per category.
Production-safe
By default
Adversarial input ramps into rate-limited test endpoints first.
Per-app
Tradecraft
Tradecraft tailored to RAG, agent, fine-tuned and frontier-model deployments.
Continuous
Drift catch
Re-validate after every model or prompt change.
For the board
For the executive: the AI risk question regulators are about to ask
EU AI Act, NIST AI RMF, sector regulators — all converging on the same question: "How do you validate the security of the AI systems you deploy?"
KeenSafe produces evidence-backed answers, mapped to the framework of choice.
Technical validation
AI validation methodology
Adversarial test suites tailored to LLM, RAG and agent architectures. Continuous re-validation hooks into model / prompt / tool changes.
- 01Architecture mapping: model, prompt template, tools, retrieved context, downstream actions
- 02Direct prompt injection across input surfaces
- 03Indirect injection via retrieved / referenced content
- 04Tool-call abuse (parameter injection, unauthorized side-effects)
- 05Agent multi-turn jailbreak + training-data extraction
Get Started
Validate your AI application before regulators ask
A guided session against your live AI app — prompt-injection chains validated and mapped to OWASP LLM Top 10.