LLM Security

What is LLM Security?

LLM security (Large Language Model security) is the set of controls, architectures, and monitoring practices required to deploy language models safely in production environments. Unlike traditional software, LLMs are non-deterministic, trained on internet-scale data, and capable of generating unexpected outputs — which makes them a distinct category of risk that traditional AppSec and API security tooling was not designed to address.

Key Threats

The most significant LLM security threats include: prompt injection (malicious input hijacking model behavior), training data poisoning (corrupting model behavior at the training stage), model inversion (extracting training data through targeted queries), jailbreaking (bypassing safety filters through adversarial prompting), data exfiltration (models inadvertently leaking sensitive information from their context), and supply chain attacks targeting the model weights, inference infrastructure, or fine-tuning pipelines that teams use to customize models for their applications.

Input vs Output Security

LLM security operates at two distinct control points. Input security focuses on what enters the model: sanitizing user prompts, detecting injection attempts, enforcing content policies, and preventing sensitive data from appearing in context windows. Output security focuses on what the model generates: validating that responses comply with business rules, scanning for PII or credentials in completions, blocking harmful content, and verifying that AI-generated code does not introduce security vulnerabilities before it executes.

Compliance Considerations

Regulated industries face specific LLM compliance requirements. HIPAA requires that PHI not appear in prompts sent to external AI providers unless a BAA is in place. GDPR restricts what personal data can be processed by third-party AI systems and in which jurisdictions. SOC 2 requires audit trails for all data processing, including AI inference. PCI DSS prohibits cardholder data from appearing in AI prompt logs. These requirements demand both technical controls (PII redaction, data residency enforcement) and audit infrastructure.

How G8KEPR Secures LLMs

G8KEPR's Verification Engine provides end-to-end LLM security across the full request-response lifecycle. Inputs are scanned for injection patterns and PII before reaching the model. Outputs are validated against configurable content policies, PII redacted, and inspected for anomalous patterns that may indicate model manipulation. All LLM interactions are logged with full context for compliance audits. G8KEPR integrates with OpenAI, Anthropic, Google, and any OpenAI-compatible endpoint.

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