AI and LLM Application Penetration Testing: An OWASP Top 10 for LLM Applications (2025) Guide for Indian GenAI Teams
LLM penetration testing for Indian GenAI teams: which OWASP LLM Top 10 (2025) risks matter for chatbots, RAG and AI agents, and how testers check them.
AI and LLM Application Penetration Testing: An OWASP Top 10 for LLM Applications (2025) Guide for Indian GenAI Teams
LLM penetration testing for Indian GenAI teams: which OWASP LLM Top 10 (2025) risks matter for chatbots, RAG and AI agents, and how testers check them.
LLM penetration testing is a security test of an application built on a large language model, such as a chatbot, a RAG assistant over your documents, or an AI agent that can take actions. Testers try to make the AI do what it should not: reveal hidden instructions, leak customer data, misuse its tools or run up your bill. The main checklist is the OWASP Top 10 for LLM Applications (2025).
One thing to be clear on: this is about testing your AI product. It is not the same as using AI tools to pentest your other systems.
Why normal pentests miss AI risks
A normal web pentest checks code paths that behave the same way every time. An LLM does not. The same question can get a different answer, and the "input" can be any sentence in any language.
That creates new ways in. An attacker does not need a bug in your code. They can hide an instruction inside a PDF, an email or a web page your AI reads, and the model may follow it. The classic web risks still apply too, because the chatbot sits inside a normal web app with logins, APIs and databases.
The OWASP Top 10 for LLM Applications (2025) in one line each
Two entries are new in 2025: System Prompt Leakage and Vector and Embedding Weaknesses.
What to test depends on what you built
Most Indian GenAI products fall into one of three shapes. Each shape has different top risks.
Shape 1: A chatbot or assistant over your documents (RAG)
a support bot that answers from your policy PDFs, or an internal assistant over HR and finance files.
plant instructions inside an uploaded document; ask questions that pull data from another customer's or another department's files; check whether the vector store respects user permissions. A RAG system that searches everything for everyone is a data leak waiting to happen.
Shape 2: An AI agent that takes actions
an assistant that can raise a refund, update a CRM record, send an email or query a database.
trick the agent into calling a tool it should not, with values the user should not control. They check whether actions need human approval and whether each tool runs with the least access possible. This is where small prompt tricks turn into real business damage.
Shape 3: An AI feature inside an existing product
a "summarise this ticket" button, an AI-written email draft, or an AI classifier for loan documents.
inject content that the AI repeats back into your web page or downstream system; check whether summaries pull in data the user should not see; and send large or repeated requests to see if limits exist.
Every shape should also be checked for LLM07. Treat your system prompt as something an attacker will eventually read, and never put API keys, passwords or internal rules you cannot afford to leak inside it.
An Indian detail many teams miss
Indian users switch languages all the time. They type in Hindi, in Hinglish, in Tamil or in English written in a regional style. Guardrails tested only in formal English can miss the same request phrased in another language or script.
A good LLM pentest for an Indian product should test prompts and jailbreak attempts in the languages your users actually use.
Personal data matters too. If customers paste PAN numbers, health details or account information into your chatbot, that data flows into prompts, logs and sometimes third-party model providers. India's DPDP Act brings substantive data protection obligations into force on 14 May 2027, so it is worth knowing now where that data goes. A compliance pre-assessment can map those data flows before then.
How SecurityLit tests AI applications
SecurityLit's AI/LLM pentesting follows five stages:
Testing is mapped to the OWASP LLM Top 10.
If you also want to see how your detection and response teams handle an AI-led attack, that is closer to red teaming, which tests people and processes as well as technology. Red teaming gives you that adversarial testing.
When to test, and how often
Test before a GenAI feature goes live, and again whenever you change the model, the system prompt, the tools an agent can call or the data a RAG system can search. Each of those changes can open a new hole even if your code stays the same.
AI products change fast, so a once-a-year test leaves long gaps. Continuous pentesting keeps testing as releases ship.
Five questions before you ship an AI feature
Could a hidden instruction in a document or web page change what the AI does?
Can one user's question ever return another user's data?
Does every tool the AI can call have the smallest possible access?
Is there anything in the system prompt you would not want published?
Is there a limit on how much one user can make the AI do?
If you are unsure about any answer, that is where testing should start.
Frequently Asked Questions
What is LLM penetration testing?
It is a hands-on security test of an application built on a large language model. Testers try prompt injection, data leakage, tool misuse and other attacks from the OWASP Top 10 for LLM Applications to find weaknesses before real attackers do.
Which risk is ranked first in the OWASP Top 10 for LLM Applications (2025)?
Prompt injection (LLM01). It happens when user input, or content the model reads, changes the model's behaviour in ways the developer did not intend.
Is AI red teaming the same as LLM penetration testing?
They overlap. LLM penetration testing focuses on finding and proving technical weaknesses in an AI application. AI red teaming usually takes a wider view, simulating a realistic attacker and testing how people, processes and controls respond.
Do we still need a normal web pentest if we test the AI part?
Yes. The AI feature runs inside a normal application with logins, APIs, databases and cloud infrastructure. Those parts can be attacked without touching the model at all.
Can guardrails from the model provider stop prompt injection on their own?
No. Provider guardrails help, but OWASP treats prompt injection as a risk that needs layered defences: limited tool access, output checks, human approval for risky actions and regular testing.
Shipping a chatbot, RAG assistant or AI agent?
Book a free scoping call for an AI/LLM pentest with SecurityLit to find and fix prompt injection, data leakage, and tool misuse vulnerabilities.
LLM penetration testing is a security test of an application built on a large language model, such as a chatbot, a RAG assistant over your documents, or an AI agent that can take actions. Testers try to make the AI do what it should not: reveal hidden instructions, leak customer data, misuse its tools or run up your bill. The main checklist is the OWASP Top 10 for LLM Applications (2025).
One thing to be clear on: this is about testing your AI product. It is not the same as using AI tools to pentest your other systems.
Why normal pentests miss AI risks
A normal web pentest checks code paths that behave the same way every time. An LLM does not. The same question can get a different answer, and the "input" can be any sentence in any language.
That creates new ways in. An attacker does not need a bug in your code. They can hide an instruction inside a PDF, an email or a web page your AI reads, and the model may follow it. The classic web risks still apply too, because the chatbot sits inside a normal web app with logins, APIs and databases.
The OWASP Top 10 for LLM Applications (2025) in one line each
Two entries are new in 2025: System Prompt Leakage and Vector and Embedding Weaknesses.
What to test depends on what you built
Most Indian GenAI products fall into one of three shapes. Each shape has different top risks.
Shape 1: A chatbot or assistant over your documents (RAG)
a support bot that answers from your policy PDFs, or an internal assistant over HR and finance files.
plant instructions inside an uploaded document; ask questions that pull data from another customer's or another department's files; check whether the vector store respects user permissions. A RAG system that searches everything for everyone is a data leak waiting to happen.
Shape 2: An AI agent that takes actions
an assistant that can raise a refund, update a CRM record, send an email or query a database.
trick the agent into calling a tool it should not, with values the user should not control. They check whether actions need human approval and whether each tool runs with the least access possible. This is where small prompt tricks turn into real business damage.
Shape 3: An AI feature inside an existing product
a "summarise this ticket" button, an AI-written email draft, or an AI classifier for loan documents.
inject content that the AI repeats back into your web page or downstream system; check whether summaries pull in data the user should not see; and send large or repeated requests to see if limits exist.
Every shape should also be checked for LLM07. Treat your system prompt as something an attacker will eventually read, and never put API keys, passwords or internal rules you cannot afford to leak inside it.
An Indian detail many teams miss
Indian users switch languages all the time. They type in Hindi, in Hinglish, in Tamil or in English written in a regional style. Guardrails tested only in formal English can miss the same request phrased in another language or script.
A good LLM pentest for an Indian product should test prompts and jailbreak attempts in the languages your users actually use.
Personal data matters too. If customers paste PAN numbers, health details or account information into your chatbot, that data flows into prompts, logs and sometimes third-party model providers. India's DPDP Act brings substantive data protection obligations into force on 14 May 2027, so it is worth knowing now where that data goes. A compliance pre-assessment can map those data flows before then.
How SecurityLit tests AI applications
SecurityLit's AI/LLM pentesting follows five stages:
Testing is mapped to the OWASP LLM Top 10.
If you also want to see how your detection and response teams handle an AI-led attack, that is closer to red teaming, which tests people and processes as well as technology. Red teaming gives you that adversarial testing.
When to test, and how often
Test before a GenAI feature goes live, and again whenever you change the model, the system prompt, the tools an agent can call or the data a RAG system can search. Each of those changes can open a new hole even if your code stays the same.
AI products change fast, so a once-a-year test leaves long gaps. Continuous pentesting keeps testing as releases ship.
Five questions before you ship an AI feature
Could a hidden instruction in a document or web page change what the AI does?
Can one user's question ever return another user's data?
Does every tool the AI can call have the smallest possible access?
Is there anything in the system prompt you would not want published?
Is there a limit on how much one user can make the AI do?
If you are unsure about any answer, that is where testing should start.
Frequently Asked Questions
What is LLM penetration testing?
It is a hands-on security test of an application built on a large language model. Testers try prompt injection, data leakage, tool misuse and other attacks from the OWASP Top 10 for LLM Applications to find weaknesses before real attackers do.
Which risk is ranked first in the OWASP Top 10 for LLM Applications (2025)?
Prompt injection (LLM01). It happens when user input, or content the model reads, changes the model's behaviour in ways the developer did not intend.
Is AI red teaming the same as LLM penetration testing?
They overlap. LLM penetration testing focuses on finding and proving technical weaknesses in an AI application. AI red teaming usually takes a wider view, simulating a realistic attacker and testing how people, processes and controls respond.
Do we still need a normal web pentest if we test the AI part?
Yes. The AI feature runs inside a normal application with logins, APIs, databases and cloud infrastructure. Those parts can be attacked without touching the model at all.
Can guardrails from the model provider stop prompt injection on their own?
No. Provider guardrails help, but OWASP treats prompt injection as a risk that needs layered defences: limited tool access, output checks, human approval for risky actions and regular testing.
Shipping a chatbot, RAG assistant or AI agent?
Book a free scoping call for an AI/LLM pentest with SecurityLit to find and fix prompt injection, data leakage, and tool misuse vulnerabilities.