LLM Application Penetration Testing in India: OWASP LLM Top 10 for 2026

The AI Feature You Shipped Is an Attack Surface You Haven't Tested

Between 2024 and 2026, Indian enterprises moved LLM features from pilot to production at remarkable speed — customer-support chatbots on banking portals, RAG assistants over internal document stores, coding copilots wired into CI/CD, and agentic workflows that can raise tickets, send emails, and query databases on a user's behalf. Almost none of these systems go through a security assessment designed for how LLMs actually fail.

A conventional web or API penetration test will confirm your TLS configuration, session handling, and injection defences. It will not tell you whether a support chatbot can be talked into revealing another customer's KYC details, whether your RAG pipeline leaks a confidential HR document to any employee who asks cleverly, or whether an agent with email access can be hijacked by a prompt hidden inside an inbound attachment. Those are LLM-class vulnerabilities, and they need LLM-class testing.

The OWASP Top 10 for LLM Applications: What We Test

The OWASP Top 10 for LLM Applications (2025 edition) is the reference framework we scope LLM penetration tests against:

• LLM01 — Prompt Injection. Direct jailbreaks and indirect injection via documents, web pages, or emails the model reads. The single most exploited LLM weakness in real deployments.
• LLM02 — Sensitive Information Disclosure. Personal data, credentials, or proprietary content surfacing in responses — from training data, system prompts, RAG context, or other users' sessions.
• LLM03 — Supply Chain. Vulnerable base models, poisoned checkpoints or LoRA adapters, and compromised model-hosting or plugin dependencies.
• LLM04 — Data and Model Poisoning. Manipulated fine-tuning or embedding data that plants backdoors, bias, or targeted misinformation.
• LLM05 — Improper Output Handling. Model output passed unsanitised into downstream systems — enabling XSS, SSRF, SQL injection, or remote code execution through the model.
• LLM06 — Excessive Agency. Agents granted more tools, permissions, or autonomy than the use case needs, letting a hijacked session take real-world actions.
• LLM07 — System Prompt Leakage. Extraction of system prompts that contain business logic, credentials, or filtering rules attackers can then bypass.
• LLM08 — Vector and Embedding Weaknesses. RAG-specific flaws: cross-tenant retrieval, embedding inversion, and poisoned knowledge bases.
• LLM09 — Misinformation. Hallucinated facts, fabricated citations, or unsafe advice presented with confidence — a liability issue in regulated sectors.
• LLM10 — Unbounded Consumption. Denial-of-wallet and resource-exhaustion attacks through expensive queries, long contexts, or recursive agent loops.

How LLM Pentesting Differs from Web and API VAPT

Three differences matter in practice. First, the vulnerability surface is probabilistic: a prompt-injection payload that fails ninety-nine times can succeed on the hundredth, so single-shot testing produces false assurance — assessments need systematic, repeated adversarial prompting across attack families. Second, the trust boundary moves: in a RAG or agentic system, every document, web page, and email the model ingests is untrusted input, not just the user's chat box. Third, impact is contextual: the same jailbreak is a curiosity on a marketing chatbot and a reportable incident on a banking assistant with account-lookup tools.

A complete engagement therefore layers LLM-specific adversarial testing on top of the conventional stack: the APIs that front the model, the authentication and session controls around it, and the cloud infrastructure underneath all still need testing — an attacker who can't jailbreak your model may simply steal the API key that talks to it.

The India Compliance Angle

• DPDP Act 2023. If personal data flows into prompts, fine-tuning sets, or RAG indexes, you are processing digital personal data — leakage through an LLM response is a breach like any other, with obligations to notify the Data Protection Board and affected users.
• CERT-In directions. The 6-hour incident-reporting mandate applies to AI-driven data exposure. Discovering in production that your chatbot leaks PII is a reportable event; discovering it in a pentest report is a remediation item.
• RBI and sector regulators. BFSI deployments face outsourcing and IT-governance scrutiny of AI systems; banking VAPT programmes should now include conversational and agentic interfaces in scope.

Hardening Checklist Before You Test

• Separate system instructions from user content; never put secrets or credentials in system prompts
• Enforce per-user authorisation at the retrieval layer — the vector store must respect the same access controls as the source documents
• Treat model output as untrusted: encode, validate, and parameterise before it reaches browsers, shells, or SQL
• Scope agent tools to least privilege, require human confirmation for irreversible actions, and log every tool call
• Rate-limit by cost, not just request count; cap context length and agent recursion depth
• Red-team before launch and after every model, prompt, or tool change — LLM behaviour is version-sensitive

eNeoteric delivers LLM application penetration testing as part of its VAPT and threat intelligence services across India — covering chatbots, RAG systems, and agentic deployments for SaaS and fintech, healthcare, and government customers from Delhi, Bengaluru, Mumbai, and Lucknow. If you are also building AI features, our AI chatbot delivery practice bakes these controls in from design. For scoping and fixed pricing, see the VAPT pricing guide or contact us.