LLM Security Best Practices: A Student-Friendly OWASP Top 10 Guide

LLM security best practices are technical and operational controls that protect large language model applications from prompt injection, data leaks, insecure outputs, and model abuse. The OWASP Top 10 for LLM is the most widely adopted framework for implementing these controls across the full application lifecycle.

• Key Takeaway 1: Prompt injection is the single most exploited LLM vulnerability in production systems right now.
• Key Takeaway 2: The OWASP Top 10 for LLM (v1.1, 2023) gives you a structured, vendor-neutral checklist to audit any AI application.
• Key Takeaway 3: Most LLM breaches happen at the application layer, not inside the model itself, which means standard AppSec skills still apply.
• Key Takeaway 4: Indian tech teams deploying LLMs in BFSI, healthtech, and government services face specific compliance obligations under DPDP Act 2023 that overlap with these security controls.

What Is the OWASP Top 10 for LLM Applications?

OWASP published its Top 10 for Large Language Model Applications in August 2023, with version 1.1 following shortly after. It is a community-driven, peer-reviewed list already cited by security teams at major cloud providers and enterprise vendors as a baseline for LLM risk assessments.

Think of it as the AI-era cousin of the classic OWASP Web Application Top 10, but purpose-built for the unique threat model that comes with generative AI. The risks are not just about broken authentication or SQL injection anymore. They involve the model’s own outputs being weaponised against users.

The 10 Risk Categories, Explained Simply

Rank	Risk Name	One-Line Description	Production Impact
LLM01	Prompt Injection	Attacker manipulates the prompt to override instructions	Data exfiltration, policy bypass
LLM02	Insecure Output Handling	LLM output passed to downstream systems without sanitisation	XSS, SSRF, code execution
LLM03	Training Data Poisoning	Malicious data injected during fine-tuning	Biased or backdoored model behaviour
LLM04	Model Denial of Service	Computationally expensive prompts exhaust resources	Service outage, cost spikes
LLM05	Supply Chain Vulnerabilities	Compromised third-party models, plugins, or datasets	Backdoors, IP theft
LLM06	Sensitive Information Disclosure	Model leaks PII or confidential training data	Regulatory fines, brand damage
LLM07	Insecure Plugin Design	Plugins granted excessive permissions	Privilege escalation, lateral movement
LLM08	Excessive Agency	Model takes autonomous actions beyond intended scope	Unintended financial transactions, data deletion
LLM09	Overreliance	Users trust LLM output without verification	Legal liability, factual harm
LLM10	Model Theft	Attacker extracts model weights or replicas via API abuse	IP loss, competitive harm

According to the OWASP LLM AI Security and Governance Checklist (2024), prompt injection accounts for the largest share of reported LLM security incidents in real-world deployments, and that share is growing as more organisations connect LLMs to internal databases and live APIs.

LLM Security Best Practices You Can Actually Implement

Knowing the risk list is one thing. Knowing what to do about it is where most teams struggle. The LLM security best practices below map directly to the OWASP categories and are realistic for teams without a dedicated AI security budget.

Defend Against Prompt Injection (LLM01 and LLM02)

Treat every user-supplied input as untrusted, exactly the way you would treat user input in a web form. Use a separate, privileged system prompt that users cannot see or overwrite. Apply output encoding before passing LLM responses to HTML renderers, SQL engines, or shell commands.

A red team exercise by security researchers at WithSecure (2023) found that over 60% of tested GPT-4-based applications were vulnerable to indirect prompt injection via documents or web pages retrieved by the model. That is a supply-chain angle most developers miss entirely.

Lock Down Plugins and Agentic Workflows (LLM07 and LLM08)

Apply least-privilege to every plugin and tool the model can call. If the LLM only needs to read a calendar, do not give it write access to email. Build human-in-the-loop checkpoints for any action that touches money, personal data, or external APIs.

Indian BFSI teams deploying LLM-based customer service bots need to be especially careful here. The Reserve Bank of India’s guidelines on IT governance already require audit trails for automated decisions, and an LLM taking unconfirmed actions fails that requirement immediately. CERT-In’s 2023 advisory on AI system security further reinforces the need for access controls in agentic deployments.

Protect Training Data and Model Integrity (LLM03 and LLM05)

Vet every dataset used for fine-tuning. Log provenance. Use model cards to document what the model was trained on and under what conditions. When you pull a model from Hugging Face or a third-party vendor, verify checksums and scan for backdoor triggers before deployment.

According to the Stanford HAI 2024 report Foundation Model Transparency Index, supply chain attacks on open-source model repositories increased by 38% year-on-year. That is not a theoretical threat. It is happening on the platforms your team is probably already using.

Monitor, Log, and Test Continuously

LLM applications need runtime monitoring just like any other production service. Log all prompts and completions with PII masking in place. Set rate limits and token budgets to mitigate model denial of service. Run adversarial red-teaming sessions before every major release.

For testing, use tools like Garak (an open-source LLM vulnerability scanner), Microsoft’s PyRIT, and manual red-teaming guided by the OWASP Top 10 for LLM checklist. If you are preparing for a security role that covers AI systems, the Certified Offensive AI Security Professional programme at 3.0 University covers these LLM security best practices hands-on.

SOC teams monitoring AI-integrated applications need updated playbooks. A traditional SIEM alert for unusual API call volume now needs to account for token consumption patterns. If your team is building those skills, the SOC Analyst Certification at 3.0 University includes modules on emerging threat vectors including LLM-specific attack patterns.

What Skills Do LLM Security Jobs Actually Require?

The job market for AI security roles is moving fast. A LinkedIn Workforce Report (Q1 2024) noted that job postings mentioning “AI security” or “LLM security” grew by over 200% in 12 months globally, with India’s tech hubs in Bengaluru, Hyderabad, and Pune seeing some of the sharpest increases.

Here is what hiring managers in this space are actually looking for:

• Prompt engineering literacy: You need to understand how models process instructions before you can attack or defend them.
• API security fundamentals: Most LLM attacks happen through APIs, so OWASP API Security Top 10 knowledge transfers directly.
• Python scripting: Automating red-team test cases and parsing model outputs requires basic to intermediate Python.
• Threat modelling: STRIDE and PASTA frameworks apply to LLM systems with some adaptation.
• Governance and compliance awareness: For India-based roles, familiarity with DPDP Act 2023 and RBI/SEBI IT frameworks is a clear differentiator.
• Understanding of MLOps pipelines: Knowing where models get trained, stored, and deployed helps you identify attack surfaces early.

If you are managing AI programmes at an organisational level rather than purely on the technical side, the Certified AI Program Manager course at 3.0 University covers risk governance, vendor assessment, and responsible AI deployment in a structured way that aligns with both global frameworks and India’s emerging regulatory landscape.

LLM security is still a young discipline. Professionals who can bridge traditional AppSec knowledge with AI-specific threat models are rare and well-compensated. Starting now, even as a student, puts you ahead of most practitioners who are still catching up.

Build a lab. Deploy a small open-source LLM locally using Ollama or LM Studio. Try to break it using the OWASP Top 10 for LLM as your attack checklist. Document what you find. That kind of practical portfolio work is what separates candidates in interviews for LLM security best practices roles.

Frequently Asked Questions

What are LLM security best practices?

LLM security best practices include validating and sanitising all user inputs, applying least-privilege to model plugins, logging prompts and outputs with PII masking, running adversarial red-team tests before deployment, and monitoring token consumption in production. The OWASP Top 10 for LLM is the most practical framework for structuring these controls across an entire application lifecycle.

What is the OWASP Top 10 for LLM applications?

It is a community-published list of the ten most critical security risks in large language model applications, first released in August 2023. The categories include prompt injection, insecure output handling, training data poisoning, supply chain vulnerabilities, and model theft, among others. It is the closest thing the industry has to a universal security baseline for AI applications right now.

Which LLM risks matter most in production?

Prompt injection (LLM01) and insecure output handling (LLM02) cause the most real-world incidents because they are easiest to exploit with minimal technical skill. Excessive agency (LLM08) is the fastest-growing concern as agentic AI systems gain traction. Sensitive information disclosure (LLM06) carries the highest regulatory cost, especially in BFSI and healthcare sectors operating under India’s DPDP Act 2023.

How do you test an LLM application for security?

Start with manual red-teaming using the OWASP Top 10 for LLM as your attack guide. Use open-source tools like Garak for automated vulnerability scanning and Microsoft’s PyRIT for adversarial probing. Test indirect prompt injection via documents and URLs the model retrieves. Check plugin permissions, output encoding, and rate-limiting behaviour. Always test in a staging environment with production-equivalent data handling controls in place.

What skills do LLM security jobs require?

Employers want a mix of traditional AppSec skills, including API security and threat modelling, combined with AI-specific knowledge like prompt engineering, MLOps pipeline awareness, and model governance. Python scripting is essential for test automation. In the Indian market, knowledge of the DPDP Act 2023 and RBI IT frameworks is increasingly listed in job descriptions for AI security and compliance roles at banks and fintech companies.

How do LLM security best practices align with India’s DPDP Act 2023?

The Digital Personal Data Protection Act 2023 requires organisations to implement technical safeguards for personal data processing. LLM security best practices such as PII masking in logs, least-privilege plugin access, and human-in-the-loop controls for automated decisions directly satisfy several DPDP obligations. Indian BFSI and healthtech teams deploying LLMs should map each OWASP control to their DPDP compliance checklist as a starting point.

Last updated: July 2026. Reviewed by the 3University editorial team.