What is AI & LLM Security Evaluation?
AI & LLM Security Evaluation is a comprehensive assessment of artificial intelligence systems, machine learning models, and large language models to identify vulnerabilities that could be exploited by malicious actors. Our testing covers model security, data privacy, prompt injection, and AI system integrity.
This specialized evaluation ensures that your AI systems are secure from various attack vectors including prompt injection attacks, data poisoning, model inversion, and adversarial examples that could compromise model integrity and sensitive information.
Key Benefits of AI Security Evaluation
Our AI security evaluation services provide comprehensive protection for your intelligent systems.
Model Integrity Protection
Protect AI models from adversarial attacks, data poisoning, and unauthorized modifications that could compromise model performance and reliability.
Data Privacy Assurance
Prevent sensitive data leakage and protect training data from unauthorized access through comprehensive privacy assessments.
Prompt Security
Identify and mitigate prompt injection attacks, context poisoning, and unauthorized prompt manipulation vulnerabilities.
Compliance Readiness
Ensure AI systems meet regulatory requirements including GDPR, AI Act, and industry-specific compliance standards.
Risk Mitigation
Proactively identify and address AI-specific security risks before they can be exploited in production environments.
Performance Optimization
Balance security measures with model performance to maintain optimal AI system efficiency and responsiveness.
Our AI Security Testing Scope
We comprehensively test all aspects of AI and machine learning systems.
Prompt Injection Testing
Test for prompt injection vulnerabilities, context poisoning, and unauthorized prompt manipulation attacks.
Model Security Analysis
Analyze model architectures for adversarial vulnerabilities, data poisoning risks, and model extraction threats.
Data Privacy Assessment
Evaluate data handling, storage, and processing for privacy leaks and unauthorized data access.
API Security Testing
Test AI model APIs for authentication bypasses, rate limiting issues, and unauthorized access vulnerabilities.
Agent Security Evaluation
Assess AI agent permissions, action boundaries, and tool-use validation for security vulnerabilities.
Infrastructure Security
Evaluate AI deployment infrastructure, model serving systems, and supporting components for security.
Common AI Security Vulnerabilities We Find
Our AI security evaluation process identifies a wide range of vulnerabilities that could compromise your intelligent systems.
Prompt Injection
Malicious prompts that manipulate AI behavior, bypass safety controls, or extract sensitive information.
Data Poisoning
Manipulated training data that introduces backdoors or biases into AI models.
Model Extraction
Attacks that reconstruct or steal proprietary AI models through API queries and analysis.
Adversarial Examples
Specially crafted inputs that cause AI models to make incorrect predictions or classifications.
Membership Inference
Attacks that determine whether specific data points were used in training, violating data privacy.
Model Inversion
Techniques to reconstruct training data from model outputs, exposing sensitive information.
Our AI Security Methodology
Our systematic AI security evaluation methodology ensures comprehensive assessment of intelligent systems.
1. System Discovery
Identify AI models, APIs, data pipelines, and system components for comprehensive security assessment.
2. Threat Modeling
Analyze potential attack vectors and security risks specific to your AI system architecture.
3. Vulnerability Assessment
Test for prompt injection, data poisoning, model extraction, and other AI-specific vulnerabilities.
4. Privacy Analysis
Evaluate data handling, storage, and processing for privacy leaks and compliance violations.
5. Adversarial Testing
Test model robustness against adversarial examples and edge cases that could compromise performance.
6. Reporting
Provide comprehensive reports with findings, risk assessments, and actionable remediation recommendations.
Frequently Asked Questions
Everything you need to know about AI & LLM security evaluation.
AI & LLM security evaluation is a structured security assessment of artificial intelligence systems, large language models, and AI-powered applications. It tests for vulnerabilities unique to AI — including prompt injection, data poisoning, model extraction, and unsafe agent behaviour — that traditional pentesting does not cover. iSecNet evaluates your AI system's prompt handling, agent permissions, model integrations, and data pipelines to identify risks before they are exploited in production.
Web app pentesting focuses on code vulnerabilities like SQL injection, XSS, and broken authentication. AI security testing focuses on risks that emerge from the model itself — a prompt that manipulates the AI's behaviour, training data poisoned to introduce backdoors, or API queries that slowly reconstruct a proprietary model. The attack surface includes the model, its training data, its inference API, the agent's tool-use permissions, and the data it retrieves — none of which exist in a traditional web application.
iSecNet evaluates LLM-powered chatbots and assistants, RAG (Retrieval Augmented Generation) systems, AI agents with tool-use capabilities, ML classification and prediction models, computer vision systems, and any application that integrates third-party AI APIs such as OpenAI GPT, Anthropic Claude, Google Gemini, or Meta LLaMA. If your product uses AI to process user input, make decisions, or generate content, it needs security evaluation.
Prompt injection is when a user crafts a malicious input that overrides the AI system's original instructions — causing it to ignore safety controls, reveal confidential system prompts, impersonate users, or take unauthorised actions. For AI agents connected to databases, email, or APIs, a successful prompt injection can result in data exfiltration, unauthorised transactions, or full account takeover — all triggered by a single malicious message. It is ranked as the #1 risk in the OWASP LLM Top 10.
Yes — RAG systems introduce unique attack surfaces. iSecNet tests for: prompt injection through retrieved documents (an attacker plants malicious content in a knowledge base that the RAG retrieves and executes), over-privileged retrieval (the AI retrieves documents the current user should not have access to), data leakage through generated responses, and insecure vector database configurations. RAG-based customer service bots and enterprise knowledge assistants are increasingly targeted because they have direct access to sensitive internal documents.
iSecNet's AI security evaluation is scoped based on the complexity of your system — the number of AI endpoints, agent tools, data sources, and model types. Most evaluations are completed within 7–10 working days. Pricing is on a custom quote basis; contact iSecNet via the contact page for a scoping call. All engagements include NDA before access, a full technical report, an executive summary, and one free retest after remediation.
Secure Your AI Systems Today
Revolutionary cybersecurity and AI tools designed for the future of digital protection. iSecNet evaluates prompt handling, agent permissions, and model integrations to reduce abuse, leakage, and unsafe autonomous actions.