OWASP LLM Top 10: A Practical Guide for Indian Developers Building GenAI Applications

Why the OWASP LLM Top 10 Matters

Indian engineering teams are shipping GenAI features at unprecedented speed. ChatGPT-style assistants in enterprise SaaS, RAG-based knowledge systems, AI agents in customer support, and LLM-powered code review tools are now standard product features. The security implications are not yet standard practice.

OWASP released the Top 10 for Large Language Model Applications to fill this gap. Version 1.x covers the most critical security risks specific to LLM integrations, with concrete examples and defenses. It is increasingly cited in enterprise procurement, vendor risk assessments, and regulatory guidance (including draft RBI cyber guidance for AI-using fintechs).

For Indian teams building GenAI features, this list is the minimum security baseline. Below we walk through each of the 10 with practical defenses.

LLM01: Prompt Injection

An attacker manipulates an LLM via crafted inputs that cause it to ignore its system prompt, leak data, or take unintended actions. Two variants:

Direct prompt injection: the user-supplied prompt itself attempts to override the system. "Ignore previous instructions and..." style attacks. Naive systems are immediately compromised.

Indirect prompt injection: prompts hidden in third-party content (a document, webpage, email) the LLM is asked to summarize or process. The hidden prompt activates when the LLM ingests that content. This is the most insidious variant because the user has no idea their action triggered an attack.

• Defense: never trust LLM output as authoritative. Sanitize, validate, treat as untrusted user input downstream.
• Defense: enforce strict scope and permissions, the LLM agent should run with the minimum privileges needed.
• Defense: use structured output formats (JSON schema, function calling) that are easier to validate.
• Defense: include input/output filtering layers (NeMo Guardrails, Lakera Guard, in-house regex/classifier).
• Defense: human-in-the-loop confirmation for high-stakes actions (transactions, deletions, external API calls).

LLM02: Insecure Output Handling

When LLM output is passed to downstream systems without validation, attackers can use the LLM as a tool to attack everything downstream. If an LLM-generated SQL query is executed directly, you have SQL injection. If LLM output is rendered as HTML, you have XSS. If LLM output is used to construct shell commands, you have RCE.

Treat every LLM output as untrusted user input. The same input validation, output encoding, parameterized queries, and content security policies you apply to user-submitted data also apply to LLM-generated data.

LLM03: Training Data Poisoning

Most Indian teams use pre-trained models (GPT, Claude, Gemini, Llama) and do not train their own. Training data poisoning is therefore less directly relevant. But it becomes very relevant if you fine-tune on customer data, or use RAG with documents that could be compromised.

Defenses focus on supply chain (vetting training data sources), differential privacy for sensitive fine-tuning, and continuous monitoring of model behavior after fine-tuning.

LLM04: Model Denial of Service

LLMs are expensive per call. An attacker who can trigger expensive completions (large context windows, recursive agent loops, infinite tool calling) can run up massive bills or degrade service for legitimate users.

• Rate limiting per user, per IP, per API key
• Token budgets per session and per user
• Maximum context window enforcement
• Recursion / agent loop depth limits
• Cost monitoring with alerting on anomalous spend
• Circuit breakers that disable LLM features if cost thresholds are crossed

LLM05: Supply Chain Vulnerabilities

Pre-trained models, fine-tuning datasets, plugin/extension ecosystems and AI development libraries all introduce supply chain risk. A compromised Hugging Face model card, a malicious package masquerading as a popular AI library, a compromised plugin in your AI agent, any can introduce backdoors or vulnerabilities.

Treat AI dependencies like any other software dependency: source from trusted registries, pin versions, scan for known vulnerabilities, review before adopting, and monitor for compromise indicators.

LLM06: Sensitive Information Disclosure

This is the LLM risk most likely to create regulatory exposure for Indian businesses. PII, financial data, source code, internal documents, all routinely make it into LLM prompts and outputs. Risks include:

• Customer PII in prompts sent to third-party LLM APIs (OpenAI, Anthropic, Google) creates DPDP/GDPR cross-border transfer issues
• Outputs that include PII from training data or context can be cached, logged, or shown to wrong users
• Source code in prompts sent to public LLMs has caused intellectual property leaks (Samsung incident is famous)
• Health information in prompts creates HIPAA exposure for Indian healthcare-tech
• Defense: PII detection and redaction before LLM submission (tools like Presidio, Lakera, in-house regex+NER)
• Defense: enterprise LLM agreements that explicitly exclude data from training and provide data residency
• Defense: prefer self-hosted models for highly sensitive data
• Defense: comprehensive logging and access control for prompts and outputs

LLM07-LLM10: Plugin Design, Excessive Agency, Overreliance, Model Theft

The remaining four risks become important as you build more sophisticated LLM applications:

• LLM07 (Insecure Plugin Design): LLM plugins/tools receive untrusted input and execute privileged actions. Strict authn/authz, input validation, output validation, audit trails.
• LLM08 (Excessive Agency): AI agents with too many tools/permissions cause unintended damage when manipulated. Principle of least privilege, human-in-the-loop, scoped permissions.
• LLM09 (Overreliance): humans treating LLM output as authoritative without verification. UI patterns, confidence indicators, mandatory human review for high-stakes outputs.
• LLM10 (Model Theft): API-based model extraction attacks, weight exfiltration from self-hosted deployments. Rate limiting, output watermarking, monitoring for extraction patterns.

Integrating LLM Top 10 Into Your Security Program

OWASP LLM Top 10 is not a replacement for traditional application security, it is a complement. For any Indian team shipping GenAI features:

• Include LLM Top 10 in your threat modeling for AI features
• Add LLM-specific test cases to your application penetration tests
• Consider AI red teaming (covered separately in our AI red teaming guide)
• Train developers on LLM-specific risks, the same way they were trained on SQL injection and XSS
• Monitor LLM-specific telemetry: prompt patterns, completion costs, abnormal usage
• Update your data classification and DLP to cover prompts and completions