LLM security is the specific slice of AI security concerned with large language models as they are actually deployed in applications: receiving prompts built from a mix of trusted instructions and untrusted content, producing variable natural-language output, and in many cases, calling tools based on what that output says. It overlaps heavily with the broader field of AI security, but it is useful to name separately, because most of the practical risk in a typical AI product concentrates specifically around the language model's interaction with prompts and output, not around training data or model weights.

For most organizations building on top of a hosted or open-weight model they did not train themselves, LLM security is where nearly all of their own actionable risk and their own available control lives, because the model artifact itself is largely outside their control.

The core problem LLM security addresses

A language model is designed to interpret and respond to the meaning of whatever text it receives, without a built-in way to distinguish "this text is an authoritative instruction from my developer" from "this text is content I was asked to summarize, which happens to contain a sentence that reads like an instruction." That ambiguity is the root of most LLM-specific risk. It is why an attacker does not need to break into a system to influence it; they only need to get a persuasive sentence in front of the model.

LLM security exists to manage this ambiguity through layered controls, since no single instruction to the model reliably resolves it on its own.

Prompt injection is the central risk

Prompt injection, where content the model receives is crafted to override or redirect its intended behavior, is the risk most closely associated with LLM security specifically. It can be direct, where a user directly types a manipulative instruction, or indirect, where the manipulative content arrives through a document, web page, email, or tool response that the model was asked to process for an unrelated reason.

Because injection exploits the model's core function, interpreting language, rather than a coding flaw, it cannot be fully solved with the same techniques used for conventional injection attacks like SQL injection. Mitigating it relies on constraining what the model is exposed to and what it is authorized to do with what it produces, rather than achieving perfect resistance to manipulation.

Output handling is the second major risk

Whatever a model produces is not inherently safe simply because your own system generated it. If that output is passed to a browser, a shell, a database query, or another AI agent, it needs the same validation any untrusted input to that destination would need. Insecure output handling, treating model output as trusted because it came from "your own model," is a recurring cause of downstream vulnerabilities in LLM applications.

Data exposure through prompts and responses

Prompts often carry more sensitive information into a model's context than the immediate task strictly requires, and responses can echo that information back in ways that are easy to underestimate, because a fluent natural-language summary does not visibly flag which parts came from sensitive sources. LLM security includes deciding what should enter a prompt in the first place, filtering retrieved content by the requesting user's access rights, and controlling how long prompts and responses are retained in logs.

Tool use turns language risk into action risk

Once a model can call a tool, LLM security overlaps directly with the broader concern of agentic action risk. A prompt injection that would otherwise only produce a misleading sentence can, if the model has tool access, translate into an unauthorized action. This is why tool scoping, independent authorization checks, and limits enforced outside the model are treated as part of LLM security rather than a separate concern, even though the tool itself is not language-specific.

How LLM security relates to broader AI security

AI security is the wider discipline, covering model training risk, data governance, agent autonomy, and organizational maturity. LLM security is the part of that discipline focused specifically on how a deployed language model interacts with prompts, retrieved content, generated output, and tools. Most application teams spend the majority of their practical security effort here, because it is the layer closest to what they actually build and control, as opposed to the model training layer, which for most organizations belongs to a third-party provider.

A practical starting point

Treat every source of text reaching the model, other than your own fixed system instructions, as untrusted. Apply access control before retrieval, not after generation. Scope any tool the model can call to the narrowest useful operation, with authorization enforced independently of the model's request. Validate generated output before it reaches a sensitive destination. Redact or limit sensitive data before it enters a prompt, and set retention limits on logs that capture prompts and responses.

Where TELEON fits

TELEON's runtime policy enforcement and audit trail, applied through the gateway or supported middleware, address the tool-use and monitoring layer of LLM security directly, giving teams a consistent enforcement point across different models and agent frameworks.

It does not perform prompt-level input filtering, retrieval access control, or output validation logic on your behalf. Those remain application-level responsibilities that determine how much protection the runtime layer actually has to work with.

A short LLM security checklist

  1. Treat all content other than your own fixed instructions as untrusted.
  2. Apply access control to retrieval before content reaches the model.
  3. Scope tools narrowly and enforce authorization independent of the

model's request.

  1. Validate and encode generated output for its actual destination.
  2. Limit sensitive data entering prompts and set log retention accordingly.
  3. Monitor for injection attempts, unusual tool sequences, and volume

anomalies.

LLM security is not a separate discipline from application security so much as application security applied to a component that interprets language and sometimes acts on what it interprets. Naming it specifically helps teams focus on the risks that recur most often in practice: injection, insecure output handling, and the consequences of giving a language model real authority.