AI, Data & Intelligence
LLM Security
Runtime security for applications built on large language models: controls against prompt injection, sensitive data disclosure, insecure output handling, excessive agency and unbounded consumption.
Capability overview
What llm security involves
Language model applications blur the line between data and instructions. A retrieved web page, an uploaded CV or an incoming email can contain text that tries to redirect the model, and a model's output can end up executed as code, rendered as HTML or used to call a tool. LLM security designs defences for these new paths.
Controls follow the OWASP Top 10 for LLM Applications and are layered, because no single filter stops every attack: privilege separation for tools, treating model output as untrusted, isolating untrusted content in prompts, filtering sensitive data, rate limiting and monitoring for abuse patterns.
What is included
Security controls
Prompt injection defences
Separation of trusted instructions from untrusted content, input screening and limits on what the model can do after reading external text.
Output handling
Model output encoded before rendering, validated before use in queries or code, and never passed directly to system commands.
Tool and agency limits
Least-privilege tools, confirmation for sensitive actions and allow-lists for destinations such as URLs and email recipients.
Sensitive data protection
System prompts kept free of secrets, retrieval filtered by user permission and outputs scanned for personal data.
Abuse and cost controls
Per-user quotas, maximum context sizes and anomaly alerts that stop denial-of-wallet attacks.
How we work
How we deliver llm security
Application review
Prompt structure, data sources, tools, output destinations and user roles documented.
Risk mapping
OWASP LLM risks mapped to the application's specific data and action paths.
Control design
Layered defences selected with engineering teams, balancing protection with user experience.
Implementation support
Guardrail services, validation code and monitoring built into the application.
Verification testing
Attack scenarios replayed to confirm controls work and to set a regression test baseline.
Related capabilities
Related capabilities in Responsible AI & AI Security
AI Red Teaming
Adversarial exercises in which specialists try to make your AI system misbehave, including jailbreaks, harmful or biased outputs, data leakage and misuse of tools, before real users or attackers do.
Prompt Injection Risk Assessment
A focused assessment of how exposed your RAG applications, copilots and AI agents are to direct and indirect prompt injection, and what an attacker could achieve through it.
AI Privacy & Data Protection
Privacy by design for AI systems: lawful data use for training and inference, DPDP Act and GDPR obligations, data minimisation, retention, vendor terms and handling of individuals' rights when AI is involved.
Bias & Fairness Assessment
Measurement of whether an AI system produces unequal outcomes or error rates for different groups of people, with statistical analysis, root cause investigation and practical mitigation options.
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Questions & answers
Questions about LLM Security
Cannot find what you need? Our team responds to technical and commercial questions within one business day.
Ask a questionNot with current technology. The practical goal is to limit what a successful injection can achieve, through restricted permissions, human confirmation and output validation, so the impact stays small.
They help detect known attack patterns and unsafe content, but attackers adapt. Architectural controls such as privilege separation matter more and should come first.
Assume they can be extracted. They should not contain credentials, internal URLs or logic whose disclosure would create a vulnerability.
A review and control design for one application typically takes two to three weeks, with implementation effort depending on how many tools and data sources the application uses.
Next step
Discuss llm security with Acmez
Share what you need to change, build, integrate or support. We will map the practical next step.