AI & LLM Security Testing

Two sub-areas, one engagement model

Most organizations need both: user-facing LLM experiences and back-office agents that call APIs and tools. We keep them under one offering so scoping stays honest—your test plan reflects the system you run, not a vendor SKU split.

Chatbots, copilots, and retrieval-augmented products

We probe how models interact with your users, data stores, and retrieval pipelines in realistic conditions—where prompt-level tricks meet application-level trust mistakes.

• Prompt injection and instruction override paths that change system intent
• Unsafe or unapproved actions triggered through natural language (including via indirect prompts)
• Data leakage across tenants, sessions, or documents exposed to retrieval
• Weaknesses in content handling, uploads, and downstream rendering
• Monitoring and logging gaps that slow incident response for AI-specific abuse

Permissions, tools, and workflow integrity

When agents can read mail, update tickets, call internal APIs, or run code, the attack surface shifts from “model safety” to “who can cause side effects, and how.” We stress those boundaries.

• Tool and plugin allowlists: scope creep, confused deputy problems, and over-privileged actions
• Credential and secret handling across agent memory, logs, and traces
• Orchestration failures: retries, fan-out, and partial commits that attackers can weaponize
• Human-in-the-loop bypass and escalation paths that should never fire unattended
• Integration testing with your identity stack (SSO, service accounts, delegated auth)

• Scoped attack narratives with reproduction steps grounded in your deployment
• Risk-ranked remediation: platform controls, prompt/tool policy, and engineering fixes
• Optional purple-team sessions to align detections with observed failure modes