Emerging risk · AI-native threats
LLM / RAG / Agents security
When models read your docs, call your APIs, and act on behalf of users, the attack surface stops looking like a traditional app. We hunt prompt-layer bypasses, retrieval manipulation, and agent behaviors that become incidents at scale.
Assessment themes
Engagements are tailored to your stack—customer support copilots, internal knowledge assistants, autonomous agents with tool access, or multi-step workflows spanning MCP and enterprise integrations.
- Prompt injection & jailbreak resilience across channels (web, chat, email-to-ticket)
- RAG integrity: poisoning vectors, access-control gaps in retrieval, sensitive chunk bleed
- Tool / function calling: confused deputy problems, unsafe approvals, schema tricks
- Agent loops: runaway actions, missing human gates, unsafe replanning under pressure
- Data leakage: cross-tenant echoes, system prompt extraction, training-data inference risks
- Supply chain: third-party models, embeddings providers, document parsers in the loop
What you receive
Scenario-based findings with reproduction recipes, severity grounded in business impact, and controls you can measure—guardrails, monitoring hooks, and safer defaults for prompts and tools.
Ideal for
Teams shipping LLM features to production, platforms exposing agents to external users, and security programs standing up AI governance without slowing innovation to zero.