Enterprises are putting large language models into production faster than they are securing them. In its 2026 AI Trust Maturity Survey, McKinsey found that 72 percent of respondents cite cybersecurity as a highly relevant AI risk, yet active mitigation lags behind awareness across nearly every risk category. That gap is the whole problem in one sentence. The threat is understood at the board level, but the controls that would actually close it are not in place. This post is for the people who have to close that gap: CISOs and security teams who own the risk when an LLM application leaks data, executes attacker-supplied instructions, or quietly drifts out of policy. It walks through what makes LLM security different from the security you already run, the specific risks worth losing sleep over, and the best practices that hold up under audit rather than just sounding good in a slide deck. McKinsey & Company
What is LLM security, and why does it matter?
LLM security is the practice of protecting large language models, the data they touch, and the applications built around them across the full LLM lifecycle: training, deployment, and everything users do with the system once it is live. It covers the model itself, the pipeline that feeds it, the prompts and user input that reach it, and the model outputs that flow back into your business. Think of it as application security with a probabilistic core you cannot fully predict.
LLM security matters because the technology fails in ways traditional security tools were never built to catch. A conventional app has a defined input space and deterministic logic. An LLM accepts open-ended natural language, generates non-deterministic responses, and can be steered by text that looks completely benign to a scanner. Your existing security controls still matter, but they were not designed to evaluate model behavior or judge whether an answer is safe. That is the shift security teams have to absorb.
The stakes are higher because LLM deployments tend to sit close to sensitive data. Retrieval pipelines pull from internal knowledge bases. Agents get handed credentials and tool access. When something goes wrong, it rarely stays small, and the blast radius reaches the data you are most obligated to protect.
How is LLM security different from traditional security?
Traditional application security assumes you can enumerate the attack surface and write rules against it. LLMs break that assumption. The same input field that answers a customer question can be used to extract training data, override the system prompt, or trick the model into calling a backend tool it should never touch. You cannot fully sanitize natural language the way you sanitize a SQL parameter, because the "malicious" payload is often just persuasive English.
Conventional security tools, like web application firewalls and static scanners, look for known-bad patterns. LLM risks frequently have no signature. A prompt injection attack can be phrased a thousand ways, and tomorrow it will be phrased a thousand and one. This is why generative AI security leans so heavily on monitoring model responses and constraining what the model is allowed to do, rather than trying to block every bad input at the door.
There is also a supply chain dimension that traditional application security mostly ignores. With an LLM you inherit risk from pre-trained models, third-party fine-tuning data, vector databases, and a stack of libraries, any of which can carry poisoned data or hidden vulnerabilities. Securing an enterprise LLM means accounting for components you did not build and often cannot fully inspect. If your team is already mapping this terrain, our guide to enterprise AI security best practices goes deeper on building a program around it.
What are the top LLM security risks and vulnerabilities?
The most useful starting point is the OWASP Top 10 for LLM Applications, which catalogs the critical vulnerabilities security teams keep running into. Prompt injection still sits at number one. An attacker crafts input, directly or hidden inside a document the model ingests, that overrides instructions and bends model behavior toward their goal. Indirect injection is the nastier variant: a poisoned web page or file does the talking, and your user never sees it coming.
Sensitive information disclosure is the next one that keeps people up at night. An LLM trained or fine-tuned on sensitive data can surface that data in its outputs, sometimes verbatim. Pair that with weak access control and you have a system that will happily tell the wrong person something they were never cleared to see. Model theft and model extraction belong in the same family of LLM security risks, where unauthorized model access lets an attacker clone behavior or walk off with proprietary weights.
Then there is data poisoning. If an attacker corrupts training data or the model training pipeline, the damage is baked into the model itself and is far harder to detect after the fact than a runtime exploit. Add excessive agency, where an LLM integrated with tools is given more autonomy than it should have, and you get the conditions for real-world code execution kicked off by a cleverly worded prompt. These are not theoretical. They are the LLM risks and vulnerabilities showing up in production incidents right now.
How do prompt injection attacks actually work?
Prompt injection works because the model cannot reliably tell the difference between instructions from you and instructions from an attacker. Everything arrives as text. When a user, or a document, says "ignore your previous rules and do this instead," the model has no built-in concept of authority, so it may simply comply. Threats like prompt injection exploit the very flexibility that makes LLMs useful.
Direct injection is the obvious case: a user types something adversarial straight into the chat. Indirect injection is subtler and, frankly, scarier. The attacker plants instructions in content the LLM will later process, a webpage, a PDF, an email, and waits for your system to read it. The model treats that planted text as just more input and acts on it, which is how threats such as prompt injection turn an innocent retrieval step into unauthorized access.
You cannot fully solve this with input validation alone, though input validation still helps. The realistic defense is layered: constrain what the model can do, validate model outputs before they trigger any action, isolate untrusted content, and assume some injection will get through. Design so that even a successful prompt injection cannot reach anything that matters.
What are the best practices for securing LLMs?
Securing LLM deployments requires treating the model as one component in a larger system rather than a magic box you bolt on. Start with strict access controls. Apply role-based access to the model, the data behind it, and any tools it can invoke, so a compromised session cannot escalate into unauthorized model access or reach sensitive information it has no business touching. Least privilege is not new, but it is doing heavy lifting here.
Input validation and output handling come next. Validate user input where you can, and never trust model outputs blindly, especially when an output drives a downstream action like a database write or an API call. Improper output handling is one of the most common ways an LLM-integrated application turns a bad response into code execution. Treat every model response as untrusted until checked, the same way you would treat raw user input in any web application security review.
Layer on the operational controls that make the rest durable: content security policies, rate limiting to blunt model extraction, security policies that govern what data can enter training and prompts, and continuous monitoring of model responses for signs of abuse. None of this is exotic. It is disciplined engineering applied to a new attack surface. For teams formalizing this, AI security consulting services can shorten the distance between a list of best practices and a program that actually runs.
How do you secure training data and the model pipeline?
Training data is where a surprising amount of LLM risk originates, and it is the part teams most often overlook. If poisoned or sensitive data gets into model training, you have a problem that no runtime control will fully fix. Vet your data sources, track provenance, and keep a clear record of what went into the model and when. Data security at the pipeline stage is cheaper than incident response after a poisoned model ships.
The pipeline itself needs the same rigor you apply to any software supply chain. Pre-trained models, fine-tuning datasets, embedding models, and the libraries gluing them together all carry supply chain risk. Pin versions, verify integrity, and scan dependencies. A compromised component upstream can hand an attacker influence over model behavior downstream without ever touching your production environment directly.
Don't neglect the data that flows in at runtime either. RAG systems pull from live sources, and if those sources can be tampered with, you have reopened the data poisoning door at inference time. The same discipline that protects training data, validating sources and controlling what reaches the model, applies to the retrieval pipeline feeding your LLM applications every day.
How do you test and assess LLM security?
You assess an LLM the way you assess anything else you do not fully trust: you attack it. Penetration testing for LLM applications looks different from a standard engagement because the payloads are linguistic, but the goal is identical. Find the prompt injection that works, the path to sensitive data, the tool call that should have been blocked. Offensive security against your own model surfaces the gaps before someone less friendly does.
A proper security assessment goes beyond the model to the whole system: the API security around your endpoints, the access control on your data stores, the output handling in your application logic. Map it against a framework so you are measuring against something real. The NIST AI Risk Management Framework and its Generative AI Profile give you a structured way to govern, map, measure, and manage these risks rather than improvising. Frameworks turn a pile of findings into a defensible program.
Red teaming should be continuous, not a one-time gate. Models get updated, prompts get rewritten, new tools get connected, and each change can reopen a closed hole. Build security assessment into the LLM lifecycle the same way mature teams build it into their secure SDLC. AI security posture management is an ongoing discipline, not a launch checklist you file away.
Where does LLM security fit into your broader security program?
LLM security is not a separate empire. It fits into your broader security strategy as an extension of the controls you already run: identity, access control, data security, monitoring, incident response. The mistake is treating AI as exempt from the rules everyone else follows. The better move is extending those rules to cover model behavior, model access, and the new attack surface the model introduces. Our perspective on how AI fits into enterprise security strategy gets into how to sequence that without stalling the business.
Practically, that means your security operations team needs visibility into LLM applications the same way it has visibility into everything else. Logs, alerts, and anomaly detection should cover model inputs and outputs. When something looks off, your existing security tools and runbooks should pick it up, not route around the AI system because nobody owns it. Ownership ambiguity is itself a vulnerability.
It also means governance. Security policies, model access rules, and content security policies all need an owner and a review cadence. The organizations pulling ahead are the ones treating AI security posture management as continuous evidence, not an annual attestation. That is the difference between a program that passes an audit and one that survives a real incident.
You can pressure-test where your own program stands with our AI Blind-Spot Assessment. It is a fast way to surface the gaps between the risks you know about and the controls you have actually deployed, which, going back to that McKinsey finding, is exactly where most enterprises are exposed. If you would rather talk it through with someone who has done this across regulated environments, reach out to our team and we will help you build the LLM security program your deployments need.
Key things to remember
- LLM security is the practice of protecting large language models, their data, and their applications across the full lifecycle; it matters because LLMs fail in ways traditional security tools were not built to catch.
- LLMs change the attack surface: open-ended natural language input, non-deterministic model outputs, and supply chain risk inherited from pre-trained models and third-party data.
- The OWASP Top 10 for LLM applications is the baseline risk catalog: prompt injection, sensitive information disclosure, data poisoning, model theft, excessive agency, and improper output handling lead the list.
- Prompt injection attacks work because the model cannot separate trusted instructions from attacker-supplied text; defend in layers, since input validation alone will not stop it.
- Best practices for securing LLMs: strict access controls, input validation, careful handling of model outputs, rate limiting against model extraction, and continuous monitoring of model responses.
- Secure the training data and the pipeline, not just the running model; data poisoning and supply chain compromise are baked in early and hard to detect later.
- Test with penetration testing and continuous red teaming, measure against the NIST AI RMF and OWASP, and fold LLM security into your broader security program rather than running it as a side project.
