What is AI & LLM Security Evaluation?
Shipping an AI product without adversarial security testing is like launching a web application without ever running a pentest. The surface looks fine — until someone who knows what to look for starts probing it.
AI and LLM security evaluation is the structured process of testing your intelligent systems the way a real attacker would. That means attempting prompt injections, probing model boundaries, testing what sensitive data can be extracted through careful querying, and pushing every agent permission to its limit.
Key Benefits of AI Security Evaluation
What Your Business Gains From an AI Security Evaluation Intelligent systems carry intelligent risks. Here is what a proper evaluation delivers:
Model Integrity Protection
Adversarial inputs, data poisoning attempts, and model extraction attacks can silently degrade or compromise your AI's behaviour. We test every known manipulation technique so your model performs exactly as intended — in every condition.
Data Privacy Assurance
Training data leakage, membership inference attacks, and model inversion techniques can expose confidential information through normal-looking queries. We test what your model reveals — and make sure the answer stays within acceptable boundaries.
Prompt Security
Prompt injection is ranked the number one risk in the OWASP LLM Top 10 for good reason. We test direct injections, indirect injections through retrieved content, and context poisoning attacks that make your AI ignore its own instructions.
Compliance Readiness
The EU AI Act, GDPR, and sector-specific AI regulations are creating new mandatory security obligations for AI systems. Our evaluation identifies every gap between your current deployment and what regulators will expect — before deadlines force your hand.
Risk Mitigation
Before Go-Live Finding an exploitable prompt injection or unsafe agent permission after launch costs significantly more than finding it before. An evaluation before deployment is the lowest-cost, highest-impact security investment an AI product team can make.
Performance Without Compromise
Security controls that cripple model performance get bypassed or removed. We help you implement protections that hold up in production without degrading the user experience your product was built to deliver.
Our AI Security Testing Scope
What We Test in Every AI Security Engagement Every layer of your AI system is a potential attack surface. We cover all of them.
Prompt Injection Testing
The number one risk in the OWASP LLM Top 10 — and the most actively exploited vulnerability in deployed AI products today. We test direct injections through user input, indirect injections planted inside retrieved documents and tool outputs, system prompt extraction attempts, and context poisoning techniques that make your AI override its own safety instructions.
Model Security Analysis
Your model's architecture is not just a technical asset — it is a business one. We test for adversarial input vulnerabilities that cause incorrect outputs, model extraction attacks that reconstruct proprietary training through systematic API queries, and data poisoning scenarios that introduce hidden backdoors into model behaviour without leaving a visible trace.
Data Privacy Assessment
Sensitive information does not always leak through broken authentication. Sometimes it surfaces through the model itself. We run membership inference tests to determine whether private records are recoverable from model responses, model inversion attempts to reconstruct training data, and end-to-end data handling reviews to identify where your AI pipeline creates unintended privacy exposure.
API Security Testing
Your AI model API is the front door to everything your model knows and does. We test every endpoint for authentication weaknesses, authorisation bypasses, rate limiting gaps that enable model scraping, and parameter manipulation attacks that extract outputs your system was never designed to share publicly.
Agent Security Evaluation
An AI agent with the wrong permissions and a successful prompt injection is not just a security incident — it is an autonomous one. We assess every tool your agent can access, test whether action boundaries hold under adversarial conditions, and verify that no manipulation technique can push your agent outside its intended operating scope.
Infrastructure Security
The model is only as secure as the environment it runs in. We evaluate your AI deployment infrastructure — model serving systems, vector databases, data pipelines, and supporting cloud components — for misconfigurations, access control gaps, and exposure points that sit outside the model itself but directly affect its integrity.
Common AI Security Vulnerabilities We Find
The AI Vulnerabilities Most Products Ship With — And Never Know About Standard security testing does not catch these. A dedicated AI security evaluation does.
Prompt Injection
Ranked the number one risk in the OWASP LLM Top 10. A single crafted input overrides your AI's original instructions — silencing safety controls, leaking system prompts, impersonating users, or triggering unauthorised actions. For AI agents connected to tools and APIs, one successful injection can cause damage an entire security team spends weeks unravelling.
Data Poisoning
The attack nobody sees coming because it happens before deployment. Manipulated training data plants hidden backdoors inside the model — everything looks normal in testing, but specific trigger inputs produce attacker-controlled outputs in production. By the time it surfaces, the model has been compromised for months.
Model Extraction
Your proprietary model represents months of data collection, compute spend, and fine-tuning investment. Model extraction attacks systematically reconstruct that model through carefully designed API queries — stealing your intellectual property without ever touching your servers, your code, or your training pipeline directly.
Adversarial Examples
Inputs crafted specifically to make your AI produce wrong answers with high confidence. In fraud detection, content moderation, medical diagnosis tools, or any model where wrong decisions carry real consequences, adversarial examples are not an academic concern — they are a production liability.
Membership Inference
An attacker probes your model to confirm whether specific records — patient data, financial records, personal identifiers — were included in its training set. Even without accessing your database directly, this attack turns model query responses into a data privacy violation with direct GDPR and regulatory consequences.
Model Inversion
The inverse of data privacy. Through repeated, structured queries against your model's outputs, an attacker gradually reconstructs sensitive information that was embedded during training. No direct data access required — just a publicly available API and enough patience to pull confidential data out one inference at a time.
Our AI Security Methodology
How iSecNet Tests Every AI System — Six Phases, Zero Shortcuts Real AI security takes more than running a scanner. Here is exactly what happens from the first conversation to the final sign-off.
1. System Discovery
Before testing anything, we map everything. Every AI model endpoint, data pipeline, agent tool connection, vector database, and third-party API integration gets catalogued — including components your team may have forgotten are still active. You cannot secure what you have not identified.
2. Threat Modeling
Not every AI system carries the same risks. A customer-facing chatbot faces different attack scenarios than an internal RAG knowledge base or an autonomous agent with database access. We analyse your specific architecture and prioritise testing effort against the attack vectors that carry the highest realistic impact for your deployment context.
3. Vulnerability Assessment
Manual testing across the full OWASP LLM Top 10 — prompt injection in every input channel, data poisoning scenario evaluation, model extraction probing, insecure output handling, and sensitive information disclosure testing. Every finding is confirmed by a human tester before it enters your report.
4. Privacy Analysis
We run membership inference tests to determine what personal records your model can be made to confirm, model inversion attempts to reconstruct training data from outputs, and a full review of your data handling pipeline to identify where privacy obligations under GDPR, HIPAA, or the EU AI Act may be at risk.
5. Adversarial Testing
We craft inputs specifically designed to make your model fail — wrong classifications, bypassed safety controls, overridden instructions, and edge cases that normal QA testing never reaches. If there is a condition under which your AI behaves unsafely, this phase finds it before a malicious user does.
6. Reporting
Every finding is manually verified, documented with proof-of-concept evidence, and mapped to a remediation action your team can act on immediately. Executives get a clear business-impact summary. Technical teams get granular detail. Compliance teams get framework-mapped findings ready for regulatory submission. One free retest is included after fixes are applied.
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.
Your AI Is Handling Real Users. Is It Ready for Real Attackers?
Every unaudited AI system in production is an open invitation. iSecNet's certified AI red team tests your prompt handling, agent permissions, and model integrations — finding every abuse path, leakage vector, and unsafe autonomous action before someone outside your organisation does.