Secure Your AI Before It Becomes a Liability
AI and machine learning systems introduce an entirely new attack surface that traditional security testing cannot address. From large language models vulnerable to prompt injection to computer vision systems susceptible to adversarial perturbations, your AI infrastructure requires specialized offensive testing. Apphaz delivers deep, hands-on AI/ML penetration testing that uncovers the vulnerabilities attackers will find first — before they weaponize your own models against you.
What we test
Our testers systematically evaluate every attack vector relevant to this assessment type.
Prompt Injection & Jailbreaking
We craft sophisticated direct and indirect prompt injection attacks to bypass your LLM guardrails, extract system prompts, override safety filters, and manipulate model behavior. Our testing covers both user-facing chatbots and backend LLM integrations where injected instructions can chain through multi-step workflows to trigger unintended actions, data leaks, or privilege escalation.
Model Extraction & Theft
Attackers can reconstruct your proprietary models by systematically querying your API endpoints. We simulate model stealing attacks using prediction API abuse, hyperparameter inference, and decision boundary mapping to determine whether your intellectual property can be replicated. We evaluate rate limiting, output sanitization, and API access controls that should prevent extraction.
Training Data Poisoning
We assess your ML pipeline for data poisoning vulnerabilities — from upstream data sources and labeling workflows to fine-tuning datasets. Our testers evaluate whether adversaries can inject malicious samples that cause targeted misclassification, introduce backdoor triggers, or degrade model accuracy over time without detection by your monitoring systems.
Adversarial Input Attacks
We generate adversarial examples — imperceptible perturbations to images, text, or audio — that cause your models to produce incorrect outputs with high confidence. Testing covers evasion attacks against classification systems, object detection bypass, NLP model manipulation, and robustness evaluation under FGSM, PGD, C&W, and other established attack methodologies.
AI Supply Chain Security
Your AI stack depends on open-source models, pre-trained weights, third-party APIs, and shared datasets. We audit your AI supply chain for malicious model files (pickle deserialization attacks), compromised Hugging Face repositories, vulnerable dependencies in ML frameworks like PyTorch and TensorFlow, and insecure model serving infrastructure.
Output Manipulation & Data Leakage
We test whether your AI systems can be coerced into leaking sensitive training data through membership inference, model inversion, or carefully crafted extraction prompts. For RAG-based systems, we evaluate whether retrieval boundaries can be bypassed to access documents outside the intended scope, exposing confidential information to unauthorized users.
Our approach
A structured methodology that ensures thorough coverage and actionable results.
Scope & Model Profiling
We begin by understanding your AI architecture — model types, training pipelines, data sources, deployment infrastructure, and integration points. We identify high-risk components, define testing boundaries, and build a threat model specific to your AI/ML implementation. This includes cataloging all model endpoints, API surfaces, and data flows.
Automated & Manual Assessment
Using purpose-built AI security tooling alongside manual techniques, we probe your models for known vulnerability classes. We run adversarial attack frameworks, test prompt injection vectors, attempt model extraction through API queries, and evaluate training pipeline integrity. Every test is tailored to your specific model architecture and use case.
Exploitation & Impact Validation
When we discover a weakness, we exploit it to demonstrate real-world impact. This means extracting actual training data, bypassing safety filters to generate harmful content, manipulating model predictions to produce business-critical errors, or chaining AI vulnerabilities with application-layer flaws for maximum impact.
Reporting & Remediation Guidance
We deliver a comprehensive report detailing every finding with proof-of-concept exploits, severity ratings calibrated to your business context, and specific remediation guidance. Recommendations cover guardrail improvements, input validation strategies, output filtering, monitoring enhancements, and architectural changes to harden your AI systems against real-world threats.
Technologies and frameworks we use
What you receive
Executive Summary
A business-focused overview of your AI security posture, key risks identified, and strategic recommendations for leadership and stakeholders — written in plain language with clear risk ratings.
Technical Findings Report
Detailed documentation of every vulnerability discovered, including attack methodology, proof-of-concept demonstrations, affected model components, exploitation chains, and step-by-step reproduction instructions.
Adversarial Test Artifacts
All adversarial examples, prompt injection payloads, extraction queries, and test scripts used during the engagement — provided so your team can integrate them into regression testing and CI/CD validation pipelines.
Remediation Roadmap
Prioritized action plan with specific technical fixes, guardrail configurations, monitoring recommendations, and architectural improvements — organized by severity and implementation effort to guide your engineering team.
Secure Your AI Systems Today
Your AI models are only as trustworthy as the security testing behind them. Let our specialists find the vulnerabilities before attackers exploit them in production.