AI Application Security Testing: Securing LLMs, APIs, and Agentic Systems
The integration of Large Language Models (LLMs) and machine learning (ML) into your tech stack fundamentally alters your application’s attack surface. Traditional security boundaries are blurred when non-deterministic systems are given the authority to interpret user input as execution instructions.
AI Application Security Testing is not a standalone commodity or a basic compliance checkbox; it is a highly specialized extension of penetration testing and secure architecture design. Adding an LLM to an application introduces unique trust boundary issues that traditional security methods completely miss. We evaluate exactly how your AI system interacts with your data pipelines, your internal APIs, your users, and your cloud infrastructure to prevent sophisticated compromises before they reach production.
Engagements start from $9,500+. Pricing depends on model type (LLM/ML), data pipeline scope, exposed AI APIs/plugins, integration complexity, and whether adversarial testing is included.
Why Automated Scanners Fail at AI Application Security
Automated vulnerability scanners are built to hunt for deterministic flaws—identifying highly predictable, known issues like missing security headers, outdated libraries, or basic SQL injection patterns. They are fundamentally incapable of testing for logic abuse or natural language exploitation. Securing an AI application requires rigorous manual expert review.
When you connect an LLM to your backend databases, internal APIs, or file stores via Retrieval-Augmented Generation (RAG), you introduce complex trust boundaries. An automated scanner will not detect if a subtly crafted user prompt can manipulate your model into bypassing role-based access controls (RBAC) to summarize a document the user shouldn’t have access to. Our specialized security engineers manually probe these boundaries, simulating the exact techniques advanced adversaries use to subvert AI logic and bypass your system prompt guardrails.
Real-World Risk Scenarios: Where AI Systems Break
AI models do not exist in a vacuum. Critical vulnerabilities typically arise at the intersection of the model itself, its training data, and the surrounding application infrastructure. Our manual testing methodology targets the specific risk vectors defined by the OWASP Top 10 for LLMs and cutting-edge threat research:
Prompt Injection & Model Output Abuse
We rigorously test for both direct and indirect prompt injection. Attackers craft malicious inputs that force the model to ignore its underlying system prompt and execute unauthorized instructions. We also assess model output abuse, ensuring that if an LLM is manipulated into generating malicious payloads (such as malicious JavaScript or cross-site scripting vectors), your front-end web application properly sanitizes that output before rendering it to end-users.
Insecure API Integrations
AI applications rely heavily on APIs to fetch external data, trigger internal actions, and communicate with third-party tools. Without strict validation, an LLM can be easily manipulated into issuing unauthorized backend requests. Securing these pathways is a critical extension of our core API Penetration Testing services. We ensure your model cannot be used as a proxy for Server-Side Request Forgery (SSRF) or unauthorized data access, guaranteeing that the AI strictly adheres to the principle of least privilege.
Data Leakage Through Model Responses
Whether through flawed fine-tuning processes or overly permissive RAG pipelines, AI models frequently expose sensitive information. We aggressively test for scenarios where an LLM inadvertently leaks proprietary source code, Personally Identifiable Information (PII), API keys, or cross-tenant data belonging to other clients.
Insecure Plugin and Tool Use in Agentic Systems
Autonomous or “agentic” AI systems that have the capability to execute code, send emails, or modify databases carry the highest level of risk. We deeply test the permission boundaries of these plugins. We verify that a hijacked AI agent cannot be exploited to escalate privileges, execute remote code (RCE) on your servers, or destructively modify internal systems.
Training-Data Poisoning & Adversarial Inputs
For organizations that are actively training or fine-tuning their own ML models, the integrity of the training dataset is paramount. We assess the risk of attackers subtly altering training data to introduce logic backdoors. We ensure your models do not exhibit biased, anomalous, or explicitly malicious behavior when triggered by specific adversarial inputs or data poisoning techniques.
Core Deliverables: Actionable AI Security Outcomes
Our AI Application Security Testing methodology translates complex AI risks into clear, actionable engineering tasks. Whether you are preparing for a rigorous SOC 2 Risk Assessment or aggressively protecting your proprietary algorithms from corporate espionage, we provide deliverables that your development and executive teams can rely on.
Threat Modeling for AI Boundaries
We map the complete data flow—from raw user input, into the LLM, through the RAG pipeline, and out to third-party APIs—to identify and document architectural weaknesses.
Manual Exploitation & Bypass Testing
We provide evidence of rigorous, hands-on attempts to bypass your safety guardrails, extract underlying system prompts, and force the model into unintended states.
Integration & Access Control Review
We deliver a precise verification of whether the AI acts strictly within its defined authorization scope when executing backend functions.
Prioritized Remediation Roadmap
You receive a comprehensive, executive-ready report detailing each discovered vulnerability, its specific business impact, and exact code-level or architectural remediation steps to secure the application immediately.
Flexible Scoping for Your AI Stack
Because AI applications vary wildly in complexity and integration depth, we scope our engagements precisely to your architecture and risk profile.
- Enterprise (Adversarial & Full Pipeline Review): A comprehensive assessment for proprietary ML models and complex agentic systems. Includes deep training-data exposure reviews, advanced adversarial input testing, and full-scale penetration testing of the surrounding cloud infrastructure.
- Starter (LLM Baseline Evaluation): Ideal for web applications utilizing third-party APIs (e.g., OpenAI, Anthropic) with limited backend integration. Focuses heavily on prompt injection, basic output manipulation, and data leakage checks.
- Professional (Integrations & Agentic Abuse): Designed for complex AI applications equipped with active plugins, internal tools, and vector database integrations (RAG pipelines). We rigorously test permission boundaries, abuse automation, and complex API access controls.
Frequently Asked Questions (FAQs)
Secure Your AI Application Before It’s Targeted
Don’t leave your AI integrations vulnerable to prompt injection, logic abuse, and data exposure. Treat your AI security as a core extension of your application’s integrity. Our senior engineers are ready to scope a specialized manual assessment tailored exactly to your tech stack.