Our Methodology

AI Attack Surface

Modern AI systems introduce new attack vectors that extend beyond traditional application security and require adversarial testing approaches.

Indirect Prompt Injection

We test how external content and chained inputs influence model decisions without direct user control.

RAG Data Exposure

We assess how sensitive data is retrieved from vector stores, embeddings, and connected knowledge sources.

Jailbreak Techniques

We evaluate how models can be forced to bypass safeguards and generate restricted or unsafe outputs.

Agent & Tool Abuse

We analyze how AI agents misuse tools, trigger unintended actions, or execute insecure operations.

LEGACY THINKING

Traditional Security

  • Validates application logic and infrastructure security

  • Focuses on known vulnerabilities and static code analysis

  • Does not account for adversarial model interactions

THE 7ASECURITY WAY

AI Adversarial Testing

  • Simulates multi-step prompt injection scenarios

  • Tests system prompt leakage and context manipulation

  • Evaluates agent workflows and tool execution risks

Our Testing Methodology

AI Security Testing Workflow

01

Architecture Review

We map model flows, prompt handling, RAG pipelines, and tool integrations.

02

Adversarial Scenarios

We simulate injection chains, jailbreaks, and agent abuse across realistic attack paths.

03

Exploitable Findings

We document confirmed issues with reproducible steps and targeted remediation guidance.

How AI Systems Break

01

Prompt Injection & Instruction Manipulation

Inputs are crafted to override system instructions, bypass safeguards, or manipulate model behavior through direct and indirect injection techniques.

Sample Report
Sample Report
02

Data Exposure & Agent Misuse

Sensitive data may leak through RAG pipelines, vector stores, or model responses, while agents may trigger unintended actions due to weak validation or control logic.

Frequently Asked Questions

Yes. Our AI & LLM security testing includes prompt injection, jailbreak, and system prompt exposure testing. We assess whether attackers can manipulate model behavior, bypass safeguards, extract hidden instructions, or force the system to perform unintended actions.

Yes. We review Retrieval-Augmented Generation (RAG) pipelines, vector stores, embeddings, connected knowledge sources, and model responses to identify data exposure risks. This helps detect whether sensitive data can leak through user prompts, retrieved content, or insecure integrations.

Yes. We assess AI agents, APIs, workflows, and connected tools to identify risks such as unauthorized actions, insecure tool usage, excessive permissions, business logic abuse, and unintended execution paths.

AI & LLM security testing is a security assessment focused on AI systems, large language models, RAG applications, chatbots, AI agents, and tool-connected workflows. The goal is to identify vulnerabilities that traditional application security testing may not fully cover.

AI systems introduce new attack surfaces, including prompt injection, data leakage, jailbreak techniques, unsafe model outputs, insecure integrations, and agent misuse. Testing helps organizations identify these risks before attackers exploit them.

We review prompt handling, system instructions, model behavior, RAG data access, embeddings, vector databases, agent workflows, API connections, tool permissions, user input handling, and response validation controls.

Traditional security testing focuses mainly on application, infrastructure, and code vulnerabilities. AI adversarial testing focuses on how attackers can manipulate prompts, model behavior, context, tools, data sources, and AI decision-making workflows.

Yes. AI security testing helps reduce business risk by identifying exploitable weaknesses, sensitive data exposure, unauthorized tool actions, and unsafe model behavior before they affect customers, operations, or compliance obligations.

The assessment typically includes architecture review, threat modeling, adversarial scenario testing, prompt injection testing, RAG and data exposure review, agent/tool abuse testing, and reporting with practical remediation guidance.

Yes. Findings are documented with technical details, business impact, reproduction steps, and recommended remediation guidance so your team can fix the issues and improve the security of your AI system.
VALIDATION

Success Stories

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Identify exploitable AI security risks before they impact your business. Schedule a comprehensive AI & LLM security assessment and receive clear, actionable findings to strengthen your AI applications.

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AI & LLM Security Assessment

Evaluate prompt injection, jailbreak techniques, RAG data exposure, agent workflows, tool integrations, and other AI-specific attack vectors through expert manual testing.

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Actionable AI Security Report

Receive a prioritized report with proof of exploitation, business impact, risk ratings, and practical remediation guidance to help your team secure AI systems with confidence.

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