AI & LLM Security Testing

advantages

A professionally performed AI security assessment gives your organization a realistic evaluation of how attackers could abuse AI functionality within your applications. As organizations rapidly integrate large language models and AI-driven workflows into products and internal tools, new attack surfaces emerge that traditional penetration tests may not fully cover.

With AI & LLM security testing, you can:

• Identify prompt injection attacks that manipulate AI systems into ignoring safeguards or exposing sensitive information.
• Detect inference attacks that allow attackers to extract confidential data through carefully crafted prompts.
• Reveal weaknesses in retrieval-augmented generation (RAG) systems that may allow manipulation of knowledge sources or leakage of internal data.
• Uncover insecure integrations between AI systems and external tools, APIs, or plugins.
• Identify privilege escalation paths where AI workflows can trigger actions beyond their intended permissions.
• Demonstrate real business risk by simulating how attackers could abuse AI features to access sensitive data or perform unintended operations.
• Strengthen trust with customers and stakeholders by demonstrating that your organization takes AI security seriously.

compare

Automated tools can assist with basic AI security checks, but relying solely on automation creates significant blind spots when assessing modern AI systems.

Many AI vulnerabilities depend on context, prompt manipulation, or chained interactions between models, data sources, and application logic. These issues cannot be reliably detected by scanners.

Automated tools often miss vulnerabilities such as:

• Prompt injection attacks that override system instructions.
• Inference attacks that extract sensitive information from model responses.
• Unsafe tool or plugin invocation triggered through crafted prompts.
• Weak authorization controls within AI-powered workflows.
• Manipulation of retrieval-augmented generation (RAG) data sources.
• Logical flaws in AI application behavior that allow unintended actions.

At Artifice Security, we perform manual AI security testing using adversarial thinking and real-world attack techniques. Our consultants interact directly with AI systems to simulate how attackers manipulate prompts, chain vulnerabilities, and exploit weaknesses in AI integrations.

This hands-on approach reveals risks that automated tools cannot detect and provides organizations with actionable insight into how their AI systems could be abused in real-world scenarios.

test types

AI Application Security Testing

Modern applications increasingly rely on AI functionality for automation, decision-making, and user interaction. We test how attackers could abuse these AI features to bypass controls, manipulate outputs, or gain unauthorized access to sensitive information.

Our testing focuses on prompt handling, access controls, data exposure, and how AI functionality interacts with the surrounding application environment.

LLM Security Testing

Large language models introduce unique security challenges that do not exist in traditional software systems. Our testing simulates adversarial prompts designed to manipulate model behavior, bypass safety controls, and extract sensitive data.

We analyze how the model processes instructions, handles context, and interacts with other components of the application.

RAG and AI Workflow Security Testing

Many AI systems rely on retrieval-augmented generation (RAG), external APIs, and automated tools to expand model capabilities. These integrations can introduce significant security risks if not properly secured.

Artifice Security evaluates how AI systems interact with knowledge bases, internal documents, external services, and automation tools to identify opportunities for data leakage, manipulation, or unintended system actions.

vulnerabilities

AI-powered applications introduce unique attack surfaces that traditional security testing may overlook. During an AI & LLM security assessment, Artifice Security evaluates systems for vulnerabilities that go beyond the OWASP top 10 for LLM that could allow attackers to manipulate model behavior, extract sensitive information, or trigger unintended system actions.

Common issues we test for include:

• Prompt injection attacks that override system instructions or bypass safety controls

• Indirect prompt injection through external content such as documents, websites, or user-submitted data

• Inference attacks that extract sensitive information through carefully crafted prompts

• Retrieval-augmented generation (RAG) poisoning and manipulation of knowledge sources

• Unsafe tool or plugin invocation triggered by model output

• Model jailbreak techniques designed to bypass guardrails or restrictions

• Authorization flaws in AI-driven workflows and agent actions

• Sensitive data leakage through model responses or contextual memory

methodology

Artifice Security follows a proven, repeatable methodology for assessing AI applications, large language model integrations, and AI-driven workflows. Our approach is highly manual because meaningful AI security testing requires contextual analysis, adversarial thinking, and real interaction with the target system. This allows us to uncover complex vulnerabilities that automated tools routinely miss.

Each issue in our final report is validated by hand and supported with clear, repeatable proofs-of-concept so your team can understand the risk and fix it with confidence. To ensure consistency and quality, we organize every AI & LLM security assessment into the following key phases:

01

Define the Scope

Before testing begins, Artifice Security works with your team to define the exact scope of the engagement. This includes identifying the AI-enabled features, model integrations, data sources, workflows, and supporting infrastructure that will be assessed.

• Identify AI applications, LLM features, agents, APIs, plugins, or workflows to be tested

• Define which environments are in scope, such as production, staging, or isolated test instances

• Identify connected systems such as vector databases, knowledge bases, external tools, and third-party APIs

• Define exclusions, safety boundaries, and any operational constraints

• Schedule testing dates and establish communication and escalation procedures

02

Information Gathering / Architecture Review

We begin by analyzing how the AI system is designed, what data it can access, how prompts are processed, and what actions the model or workflow can trigger. This helps us identify likely attack surfaces before active testing begins.

• Review application workflows and AI feature behavior from an attacker’s perspective

• Identify model entry points such as chat interfaces, APIs, embedded assistants, and agent workflows

• Map trust boundaries between users, prompts, models, retrieval layers, tools, and downstream systems

• Identify accessible data sources including uploaded content, internal documents, and retrieval-connected knowledge stores

• Review authentication, authorization, and role boundaries that affect AI functionality

03

Enumeration & Attack Surface Analysis

Artifice Security performs active analysis of the AI system to identify exposed functionality, weak controls, and potential abuse paths. This phase focuses on understanding how the model behaves under normal and abnormal conditions.

• Enumerate available prompts, workflows, tools, and model-driven actions

• Identify insecure model access patterns and missing access controls

• Assess how the application handles context, memory, file uploads, and user-supplied content

• Analyze retrieval-augmented generation (RAG) behavior and connected knowledge sources

• Review plugin, tool, and API integrations for abuse opportunities

• Identify weak boundaries between trusted instructions, untrusted content, and model output

04

Attack & Exploitation

Using manual techniques, our consultants attempt to exploit vulnerabilities in the AI system to demonstrate real-world risk. These are controlled, ethical attacks performed carefully to avoid disruption while showing how the system could be abused by a real adversary.

• Test for prompt injection and instruction override attacks

• Attempt inference attacks to extract sensitive or hidden information through crafted interactions

• Evaluate whether model output can be used to trigger unauthorized actions or unsafe downstream behavior

• Test for RAG abuse, context manipulation, and unauthorized data retrieval

• Assess insecure plugin, tool, and agent behavior that could expand attacker control

• Chain multiple weaknesses together to simulate realistic attack paths and business impact

• Deliver clear, documented proofs-of-concept where appropriate

05

Reporting

We provide a clear, prioritized report that explains both technical risk and business impact. Every finding is manually validated to eliminate false positives and ensure your team can act on the results immediately.

Your report includes:

• A non-technical executive summary explaining the business impact of the findings

• A vulnerability list ranked by severity, exploitability, and potential downstream impact

• Step-by-step attack narratives showing how the AI behavior was abused

• Screenshots and repeatable proofs-of-concept for each validated issue

• Clear remediation guidance tailored to your AI architecture and workflows

• Optional customer-facing report and attestation letter

06

Remediation Testing (Retesting)

Once your team has addressed the findings, Artifice Security can re-test the identified vulnerabilities to confirm they were properly fixed and that the AI functionality behaves securely after remediation.

• Validate that previously identified vulnerabilities have been resolved

• Confirm that fixes did not introduce new weaknesses or break intended functionality

• Issue an updated report reflecting the current security posture

• Provide evidence for audits, customer assurance, and internal stakeholders

FAQ