Secure every large language model in production
Runtime protection, adversarial testing, and continuous monitoring for LLM deployments — covering the full OWASP LLM Top 10 from prompt injection through supply chain risk.
What is LLM Security — Protect Large Language Model Deployments?
LLM security is the practice of protecting large language model applications from exploitation, misuse, and data leakage. It encompasses input validation to block prompt injection, output filtering to prevent sensitive data exposure, access controls to restrict model capabilities, and runtime monitoring to detect adversarial usage patterns.
Why LLMs introduce security risks standard controls miss
- Prompt injection attacks manipulate LLM behaviour by embedding instructions in user input or retrieved context
- LLMs may leak sensitive training data, PII, or internal system information in responses
- Insecure output handling allows LLM responses to trigger downstream XSS, SQL injection, or SSRF vulnerabilities
- Over-permissioned LLMs with access to tools, APIs, and data stores become high-value targets for privilege escalation
A four-step operational model
Input Controls
Validate and sanitise all input entering the LLM context window — blocking injection payloads, PII, and adversarial instruction sequences.
- Prompt injection detection
- PII input filtering
- Context window sanitisation
Output Filtering
Inspect LLM responses for sensitive data, harmful content, and unintended information disclosure before delivery to clients.
- Sensitive data pattern detection
- Harmful content classification
- Output schema validation
Access Controls
Restrict LLM capabilities through minimal-privilege tool permissions, API scope limits, and per-user context isolation.
- Minimal-privilege tool permissions
- API scope enforcement
- User context isolation
Runtime Monitoring
Continuous monitoring of LLM interactions for anomalous patterns, jailbreak attempts, and extraction attacks in production.
- Anomalous query detection
- Jailbreak attempt alerting
- Exfiltration pattern monitoring
Outcomes for security teams
LLMs expand the attack surface significantly
Every LLM application creates a new attack surface that requires dedicated security controls beyond what protects traditional web applications.
Data leakage risks are non-obvious
LLMs may inadvertently surface training data, system prompts, or other users' data — without any traditional vulnerability present.
Regulatory scrutiny of AI is accelerating
EU AI Act, GDPR, and sector-specific regulations increasingly require demonstrable security controls around AI-processed data.
Direct answers
What is LLM security?+
LLM security covers protecting large language model applications from prompt injection, data leakage, insecure output handling, and misuse — using runtime controls, adversarial testing, and monitoring.
What is the OWASP LLM Top 10?+
The OWASP LLM Top 10 is a list of the most critical security risks for LLM applications — including prompt injection, insecure output handling, training data poisoning, and supply chain vulnerabilities.
How does prompt injection work?+
Prompt injection attacks embed instructions in user input or retrieved content (indirect injection) that override the model's system prompt, potentially causing the LLM to ignore safety rules, leak data, or perform unintended actions.
Does Threatstealth monitor LLM applications in production?+
Yes. The LLM security scanner provides both pre-deployment testing (adversarial test suite) and production monitoring for anomalous LLM interaction patterns.