An LLM & AI Agent Security Review is a scoped assessment of the security and operational boundaries around an AI workflow. It examines agreed systems, scenarios, access, and safe testing conditions to identify prompt-injection, data-exposure, unsafe-output, retrieval, permission, and human-handoff concerns. It focuses on the authorised AI workflow and reports the vulnerabilities, unsafe outputs, prompt-injection paths, and data-exposure scenarios observed within scope. The review gives teams a more practical basis for deciding what to address first and where further implementation or testing is needed.
What It Is
The review begins with AI workflow and architecture intake: what model or agent is used, what information it receives, what retrieval or tools are connected, what actions it can take, and where a human remains responsible. Within the agreed boundary, testing can examine prompt-injection behaviour, data-exposure paths, unsafe-output handling, retrieval risks, tool permissions, action boundaries, logging, and handoff. Findings are documented with context and proportionate remediation guidance. Retesting can be agreed after changes are made. The work stays within safe testing boundaries and the authorised scope, with methods chosen to avoid disrupting live systems.
Who It Is For
An AI security review suits teams that:
- Have an AI assistant or agent workflow with defined tools, data, users, and operating boundaries
- Need a more structured view of prompt, retrieval, permissions, output, or handoff risks before wider use
- Want findings and remediation priorities that give an accurate picture of AI security within scope
- Need to distinguish an AI workflow assessment from a broader penetration test of unrelated infrastructure
What It Helps Clarify
The review helps clarify:
- Which agreed AI scenarios, data paths, retrieval sources, tools, and actions need testing
- Where prompt injection, unsafe output, excessive permissions, tool misuse, or data exposure may require controls
- What logging, approval, fallback, and human-handoff boundaries are visible within the current workflow
- Which findings need a production fix, retest, vendor question, governance decision, or separately scoped security work
What's Included
- Written confirmation of authorised systems, scenarios, access, safe testing boundaries, and exclusions
- AI workflow and architecture intake covering models, retrieval, data, tools, permissions, actions, and handoff
- Controlled testing of agreed prompt-injection, data-exposure, unsafe-output, retrieval, and permission scenarios
- Findings report with practical context, priority, and remediation guidance
- Review of logging, action boundaries, fallback behaviour, and human approval points within scope
- Handoff discussion and possible retesting options after separately agreed remediation
What's Not Included
- A full penetration test of unrelated applications, infrastructure, networks, or vendors
- Unrestricted adversarial testing, disruption of live systems, or access outside the approved boundary
- Model training, fine-tuning, model replacement, production fixes, 24/7 monitoring, managed detection, or emergency response
- Legal advice, formal certification, compliance approval, or a guarantee of AI security
- A claim that every vulnerability, unsafe output, injection path, or data-exposure scenario will be found or eliminated
When to Choose It
Choose an LLM & AI Agent Security Review when a workflow is defined enough to identify its sources, permissions, tools, users, and decision boundaries, but the team needs an independent view of its risk posture. It is useful before a broader rollout, after an integration changes, or when a team has concerns about prompt injection, retrieval, tool actions, or sensitive information. It is less suitable when the use case, data boundary, or ownership is still unclear; workflow discovery or governance may be needed first. The review works best when authorised access, safe test scenarios, and owners for remediation decisions are available.
Our Approach
We agree the systems and safe scenarios before testing begins. The review considers how an attacker or unexpected input could influence prompts, retrieval, tool use, output, permissions, or user decisions within the agreed workflow. We avoid disruptive testing and keep the work within the approved systems. Findings distinguish observed behaviour from assumptions and explain what a proportionate next step could be. Some issues may be addressed through configuration, code, permission changes, source governance, human review, or a separate security engagement. A passing result is recorded for the tested context and configuration. The handoff gives the team a documented basis for remediation, ownership, and any retest decision. It also notes the reach of the test evidence: which areas sat outside the assessment, what depended on provided access, and where a change in model, source, tool, or rollout configuration could create a new question. This supports a more careful, ongoing follow-through.
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