LLM security: a complete guide
What LLM security is, why it is different from traditional application security, the main risks, and how to protect your LLM applications.
By Krikor Tengerian · Co-founder, SecuritAI Technologies Ltd. · June 2026
LLM security is the practice of protecting applications built on large language models from attacks that manipulate their inputs, leak their data, or hijack their actions. It covers the whole lifecycle: testing the model for weaknesses before launch, and inspecting every prompt and response in production to block attacks in real time.
Large language models turned every chatbot, copilot, and AI agent into a new kind of application, one that interprets language instead of running fixed code. That shift created a new attack surface that traditional security tools were never built to see. LLM security is the discipline that covers it. This guide explains what LLM security is, why it is different, the main risks, and how to actually protect an LLM application.
What is LLM security?
LLM security is the set of controls that keep a large language model application safe from misuse. It spans two stages. The first is testing: probing the model with adversarial inputs before launch to find where it can be manipulated. The second is runtime protection: inspecting live traffic so that attacks which slip through testing are still blocked in production. A complete LLM security program does both, because a model that was safe on launch day can be broken by a new technique a month later.
Why LLM security is different from traditional application security
Traditional application security protects deterministic code. Attackers look for bugs, misconfigurations, or stolen credentials, and defenders patch them. An LLM does not work that way. It interprets language, so the attack vector is the text itself. A model cannot reliably tell the difference between an instruction from the developer and an instruction hidden in the content it reads. That single fact is the root of most LLM security problems, and it is why a network firewall or a code scanner cannot catch these attacks. For a deeper look at the runtime side, see our guide on the AI firewall.
The biggest LLM security risks
The industry-standard reference is the OWASP Top 10 for LLM Applications. The risks that lead it:
- Prompt injection: hidden instructions that override the model’s rules or extract confidential data. See our plain-English guide to prompt injection.
- Sensitive information disclosure: responses that leak personal data, system prompts, or internal information.
- Jailbreaks: inputs that bypass safety guardrails so the model acts outside its intended scope.
- Excessive agency: for AI agents with tool access, manipulated inputs that trigger unauthorized actions, API calls, or privilege escalation.
- Supply chain and data poisoning: compromised training data, models, or retrieved content that changes the model’s behavior.
These risks are not theoretical. IBM’s 2025 Cost of a Data Breach Report found that 97% of organizations that suffered an AI-related breach lacked proper AI access controls, and that unmanaged “shadow AI” added about 670,000 US dollars to the average breach cost.
How to secure an LLM application
Effective LLM security combines two controls used together:
1. Test before you ship. Run your application against a large library of adversarial prompts, not a handful you thought up yourself. AI red teaming covers the full OWASP LLM Top 10 and returns a documented set of findings with remediation guidance, the evidence auditors and customers increasingly expect.
2. Protect in production. Testing tells you where the holes are before launch. An AI firewall catches the attacks that show up after launch, inspecting every prompt and response in real time and blocking malicious input before it reaches your model.
LLM security best practices
- Map what each LLM can see and do before you secure it.
- Adversarially test against the OWASP LLM Top 10, not just happy-path prompts.
- Inspect both inputs and outputs in production, not just one side.
- Log every prompt, response, and block decision for incident response and compliance.
- Re-test on every model upgrade, prompt change, and new feature.
- Keep sensitive data out with PII detection and redaction, and use data residency where regulations require it.
For the governance and audit-evidence side of LLM security, documenting policies, mapping controls, and proving compliance, SecuritComply automates the program so your testing and monitoring records live in one audit-ready place.
Not sure where you stand? The free AI Security Readiness Check scores your AI setup in 60 seconds, no signup.
LLM security in one platform
SecuritAI covers both halves of LLM security: AI Red Teaming to test your model before launch, and a 7-layer AI Firewall to protect it in production, with a full audit log and Canadian data residency. It works as a proxy, so you protect your AI by changing two values with no rewrite.
Choosing a vendor? See why SecuritAI is independent.
References
LLM security questions
What is LLM security?
LLM security is the practice of protecting large language model applications from attacks that manipulate their inputs, leak their data, or hijack their actions. It covers testing the model for weaknesses before launch and inspecting every prompt and response in production to block attacks in real time.
How is LLM security different from traditional cybersecurity?
Traditional cybersecurity protects deterministic code against bugs, misconfigurations, and stolen credentials. LLM security protects a model that interprets language, where the attack vector is the text itself. A model cannot reliably separate developer instructions from instructions hidden in the content it reads, so network firewalls and code scanners cannot catch these attacks.
What are the main LLM security risks?
The OWASP Top 10 for LLM Applications leads with prompt injection, sensitive information disclosure, jailbreaks, excessive agency in tool-using agents, and supply chain or data poisoning. Prompt injection is the most common and the root of many data leaks.
How do you secure an LLM application?
Use two controls together: adversarial testing (AI red teaming) before launch to find weaknesses across the OWASP LLM Top 10, and an AI firewall in production that inspects every prompt and response in real time to block attacks that testing did not anticipate.
Why does LLM security matter for compliance?
Regulators and frameworks increasingly expect documented testing, runtime monitoring, and audit logs for AI systems that handle personal or operational data. LLM security produces that evidence, which supports obligations under PIPEDA, Bill C-8, and standards like SOC 2.
Krikor Tengerian
Co-founder, SecuritAI Technologies Ltd.
Krikor Tengerian is the co-founder of SecuritAI Technologies and has over 25 years of experience in cybersecurity and IT infrastructure. He leads the company’s AI security platform and works with Canadian organizations and government bodies to secure their AI deployments against adversarial threats.
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