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Read Polygraf AI's plain-English guide to LLM security for enterprise teams to understand why securing an LLM is a must have for any organization who cares about their privacy.
Large language models are being used in email, code, customer service, legal and finance workflows. None of them had a security manual. This is the manual that fills this gap – what LLM security is, why your current tools don't provide it and what needs to be done.
LLM security is the effort to prevent misuse, manipulation, data leakage and unauthorized action of large language models and the systems on which they are based. It includes both sides of the LLM interaction: the inputs and the outputs and actions.
The simplest way to explain it: LLM is software that takes instructions in natural language as input. The same security knowledge a security team has for securing software applies: access control, input validation, audit logging, least privilege. However, natural language instructions cannot be validated like structured API parameters. An LLM that takes a malicious instruction in fluent English has no syntactic way of rejecting it. It has to evaluate it. The evaluation is where LLM specific security failures happen.
LLM security is the set of controls over what an AI system can be told to do, what data it can read and write, what it can do, and how it is observed and enforced – at the model input/output, the data, the tool, and the identity layers.
The domain of LLM security has grown substantially as LLMs have moved from the lab to production. In 2023 LLM security was the prevention of chatbots from making bad statements. In 2026 it is about LLMs embedded in email, code repositories, financial flows, clinical notes and agentic systems that take real action in enterprise infrastructure. The failure modes have grown too.
Enterprise security stacks were designed with the assumption that software is predictable. A WAF blocks requests that match known bad patterns. A DLP system blocks files that contain credit card numbers. An IAM system allows or denies access based on identity and role. These systems work because the inputs they check have a structure (HTTP requests, file types, identity tokens) that can be matched against good or bad patterns.
LLMs process free-form natural language. Same input field that allows a valid customer question also allows a jailbreak attempt, an indirect prompt injection, or social engineering payload. There is no syntactic difference between a benign and a malicious prompt. No traditional tool can perceive that difference. And when the LLM takes an action downstream (writing to a database, sending an email, calling an API), those actions are triggered by the LLM's interpretation of its instruction, not by a human calling the function.
Your security stack knows that a user was authenticated and used a resource. It doesn't know that the LLM the user was using was also processing a hidden instruction in a document it pulled and about to send your database credentials to an attacker-controlled address in a tool call that looked like legitimate API usage. This is what LLM security fills.
LLM security is not a hypothetical. The OWASP Top 10 for LLM Applications 2025 is based on attacks in production. The MITRE ATLAS – adversarial machine learning knowledge base – grew to 16 tactics and 84 techniques in 2025 with 14 new techniques for AI agents. In 2026, CrowdStrike's threat report had prompt injection attacks on 90+ organizations. The AI Incident Database has 346 AI incidents in 2025.
Five threat categories account for the majority of LLM security incidents in enterprise environments:
OWASP Top 10 for LLM Applications is the most popular reference framework for LLM security. Updated for 2025 (2nd edition) from production vulnerabilities in the wild and maintained by the OWASP GenAI Security Project. The #1 critical vulnerability is prompt injection in both editions. Knowing the list is the starting point of any enterprise LLM security program.
LLM security is not a control or a category of products. It is five layers of an enterprise AI system and has its own threat surface and controls. A program that covers only one or two layers is vulnerable in the other.
Click any layer to expand threats and controls
Every prompt and piece of content the LLM is fed is an injection vector. Indirect injection (instructions in retrieved documents, emails, web pages) now makes up over 55% of real-world attacks and has 20–30% higher success rates than direct injection.
Check the semantic input of the LLM before processing. Is the retrieved content adversarial or goal-hijacking? Check the session for multi-turn escalation.
Model weights, training data, fine-tuning datasets and system prompts are all attack surface. A poisoned model or leaked system prompt breaks every downstream control. AI Incident Database recorded 346 incidents in 2025 – many of them are caused by this layer.
Check model integrity. Protect system prompt (secret, not documentation). Scan all model components, plugins and MCP servers in the supply chain before deployment.
What the LLM returns – to users, to downstream systems, to tool calls. Sensitive data disclosure, policy violations and insecure content happen at the output layer. Cyberhaven: 11% of data employees paste into ChatGPT is confidential – the output layer is where that data is exposed externally.
Pre-transmission output inspection. PII redaction in-line. Response content policy enforcement. Content filtering. This is the main enforcement point of Polygraf – sub-100ms, on-premise, for LLM01, LLM02, and LLM05 in one pass.
RAG pipelines, vector databases and knowledge bases inject poisoned retrieval content into the LLM's context. PoisonedRAG (2024) had 97% success rate with 5 poisoned documents in a million-document knowledge base – this is the persistence layer that allows attacks to survive across sessions.
Track the provenance of every chunk retrieved. Check the retrieval time for instruction-like patterns. Tiered access: not every agent should read every knowledge base. Immutable audit trail for every write to a knowledge base.
When LLMs use tools (APIs, databases, code execution, file systems) the attack surface is every system the tools can reach. 70% of enterprise agents have more access than their human counterparts (Teleport, 2026). Replit: too much tool access + no output gate = 1,206 records deleted.
Tool allowlisting at the MCP gateway. Argument-level constraints per tool. Least privilege at task level, not team level. Purpose binding at execution layer. Unique agent identity and independent revocation path.
These are not security research demos. They are documented production incidents from enterprise environments – each one showing a specific failure on one or more of the five layers above.
LLMs are not experimental tools in 2026. They are part of the core business systems and they are trusted with real data and real actions. This makes their failures much more dangerous than the bugs of traditional software.
— LLM Security Risks in 2026, Sombrainc · February 2026The five layer model can be difficult to manage when every layer has exposure. The order of practical implementation is by risk priority – start with the layer where most incidents are occurring and work your way out.
Inspect your LLMs' output before sending it out. One single highest coverage control – detects LLM02, LLM05 and policy violations from any attack source. Last line of defence, first to deploy.
Introduce semantic input inspection for indirect injection. Tool allowlisting and argument constraints for any LLM with tool use. Fixes the EchoLeak and Replit attack paths (the two most high-profile enterprise LLM failures in the last 12 months).
Implement RAG provenance and retrieval time inspection. Supply chain audit all models, plugins and MCP servers. Add shadow AI discovery to inventory program. PoisonedRAG-class attacks.
Agent identity context structured audit logs. Behavioral baselines for each LLM deployment. Quarterly review of tool permission policy. Mapping of OWASP LLM Top 10 and NIST AI RMF annually.
Polygraf's Behavioral Control Plane is on Layers 1 and 3 at the same time: it looks at inputs before the LLM processes them and at outputs before they are sent, it enforces policy in real time with sub-100ms latency and it has full audit logging of every interaction. It covers the highest volume attack classes (LLM01, LLM02, LLM05, LLM06) in a single deployment, on-premise, without any data leaving your security perimeter.
Polygraf's Behavioral Control Plane is policy enforcement at the LLM input and output edge, scanning every prompt and response in real time, blocking injection, redacting context-sensitive data, tool policy enforcement and logging every interaction with full audit context. Sub-100ms. On-premise. No data leaves your environment.
At Polygraf, we envision a future where AI augments human capabilities without compromising safety, privacy, or ethical standards. Trust in our commitment to building this future with you.
© 2026 Polygraf AI. All rights reserved.
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