Unlock Business Value from LLMs Without the Security Risks

 LLM security refers to the practices, tools, and policies that protect Large Language Model (LLM) applications from threats like prompt injection, data leakage, and misuse. It’s critical for enterprises because LLMs process sensitive inputs and generate outputs in unpredictable ways, making them uniquely vulnerable compared to traditional software .

 Traditional AppSec tools rely on deterministic code paths and structured inputs. LLMs, by contrast, interpret natural language and operate probabilistically, meaning static scanning and rule-based validation often fail to catch LLM-specific threats like jailbreaking or hallucinated responses .

 Top risks include prompt injection attacks, sensitive data leakage, hallucinated or toxic outputs, supply-chain vulnerabilities, and compliance failures. Because LLMs operate on unpredictable, user-provided content, they’re harder to defend using traditional techniques .

 Prompt injections exploit the model's sensitivity to input phrasing, allowing attackers to override instructions, access restricted data, or trigger unauthorized actions. As more applications rely on user inputs for dynamic LLM tasks, the attack surface continues to grow .

 LLMs may memorize sensitive training data and regurgitate it in responses. They can also process and expose confidential inputs (like IP, PII, or secrets) without appropriate controls, especially when interacting with third-party APIs or external plugins.

 Levo sits in front of LLMs and filters prompts in real time, detecting malicious patterns like obfuscated instructions or indirect jailbreak attempts. It enforces structure and rewrites unsafe input, blocking attacks while preserving legitimate use .

 Levo uses integrated DLP techniques to scan and redact PII, PHI, credentials, and other confidential info in both prompts and responses. It enforces policy before data reaches the model and masks it in returned outputs .

Levo continuously logs prompts, responses, tool invocations, and user actions. It applies risk scoring and anomaly detection to highlight unusual behavior, providing visibility for security and compliance teams .

Levo enforces data minimization, redaction, policy-based routing, and keeps tamper-proof audit logs. This allows teams to demonstrate regulatory alignment, respond to subject access requests, and prevent unauthorized data retention .

Yes, Levo works across architectures. It supports both externally hosted APIs (like OpenAI) and in-house models, including fine-tuned or RAG-enabled LLMs. It enforces guardrails agnostic of model or vendor .

 Levo offers end-to-end security support that ensures secure development in earlier stages of SDLC and complete runtime security within production. This lets teams scale secure deployments without slowing down engineering velocity .

Secure apps follow principles like strict input/output filtering, sandboxing tool access, least privilege controls, red-teaming before deployment, and continuous monitoring. These are designed to treat both input and model output as untrusted by default .

Levo assigns dynamic risk scores based on behavioral patterns like excessive token usage, unexpected tool calls, or prompt structure anomalies. It flags divergences from expected use and enables intervention before risks escalate .

Levo secures the entire lifecycle, from sanitizing training data, validating prompts at runtime, enforcing output filters, to audit logging and red-teaming. This covers ingestion to output and supports CI/CD pipelines for LLMs .

Yes. Levo is model-agnostic and supports multiple vendors, including open-source and closed-source LLMs. It enables consistent guardrails and unified monitoring across varied deployments, ensuring scalability and interoperability .