MODEL ENGINEERING & HUMAN OVERSIGHT
LLM security testing
LLM security testing evaluates how an AI application behaves when inputs, retrieved content or tool requests cross its intended boundaries. NexWerk scopes testing and human controls around the complete workflow: the model, its data sources, user permissions and the actions it can take.
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What should an LLM security review cover?
A useful review starts with a defined application and threat model. Testing a model alone does not establish that its surrounding application is secure.
- Prompt injection
- Test whether user input or retrieved documents can redirect the workflow, override instructions or induce unauthorised actions.
- Sensitive-data exposure
- Review retrieval permissions, context assembly, output handling, logs and retention settings against the information each user should access.
- Tool execution
- Constrain tools to approved operations. Apply least privilege, parameter validation and explicit approval before consequential actions.
- Operational recovery
- Define monitoring, incident ownership, suspension and rollback so a failing workflow can be stopped and investigated.
What does an engagement deliver?
- An application threat model covering users, data boundaries, connected tools and unacceptable outcomes.
- A versioned test set with reproducible failure cases, documented scope and severity assessments.
- A remediation plan covering application controls, permissions, guardrails and model configuration, followed by retesting.
- Release criteria, monitoring signals and a human escalation path with named operational owners.
Healthcare, military support and government
Our worldwide sector focus includes document handling, administrative workflows, logistics support and internal knowledge access. These are areas for scoped collaboration, not claims of existing government contracts or clinical approval. High-impact uses require domain experts, accountable decision makers and a separate assessment of applicable requirements.
Common questions
Can guardrails guarantee safe AI?
No. Controls reduce specific risks within a tested scope. Model, data, tool or configuration changes can introduce new failure modes and should trigger evaluation again. Human oversight must include the ability to stop or reject actions.
Is security testing the same as model training?
No. LLM training and evaluation address task performance and model behaviour. Application permissions, data access, output handling and tool controls need security testing even when a model performs well.
Can testing use a private environment?
Hosting, access, data retention and provider terms are agreed during scoping. Private-cloud or on-premises requirements should be assessed before sensitive data is transferred.
Reference frameworks
The OWASP LLM Top 10 offers a risk taxonomy. The NIST AI Risk Management Framework provides broader risk-management guidance. Referencing them is not a certification or endorsement.
Explore AI agent implementation or discuss an evaluation for your workflow.
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