Machine Learning (AI)
Penetration Testing
A specialized offensive assessment targeting AI/ML systems, including LLMs, RAG pipelines, and agent-based workflows. Aligned with the OWASP ML Top 10.
True Adversarial ML Testing
Architecture-aware attacks beyond simple prompt testing
OWASP ML Top 10 Coverage
Complete coverage of machine learning specific vulnerabilities.
ML01: Input Manipulation Attack
Adversarial inputs designed to cause misclassification or unexpected model behavior through carefully crafted perturbations.
ML02: Data Poisoning Attack
Attacks that corrupt training data to influence model behavior maliciously, introducing backdoors or degrading performance.
ML03: Model Inversion Attack
Techniques to extract sensitive training data or reconstruct private information from model outputs and predictions.
ML04: Membership Inference Attack
Determine if specific data records were used in training, exposing privacy violations and sensitive information.
ML05: Model Theft
Extraction attacks that allow adversaries to steal, replicate, or reverse-engineer your proprietary models and intellectual property.
ML06: AI Supply Chain Attacks
Vulnerabilities in third-party ML libraries, pre-trained models, datasets, and dependencies that introduce security risks.
ML07: Transfer Learning Attack
Exploiting vulnerabilities in transfer learning processes where pre-trained models inherit or amplify security weaknesses.
ML08: Model Skewing
Attacks that manipulate model behavior through biased data distribution or strategic input patterns to degrade accuracy.
ML09: Output Integrity Attack
Manipulating or tampering with model outputs to cause downstream systems to make incorrect decisions or take harmful actions.
ML10: Model Poisoning
Direct attacks on the model itself during training or fine-tuning to embed backdoors or degrade security properties.
Attack Techniques
Advanced adversarial techniques for comprehensive AI security testing.
Adversarial Prompting
Prompt injection attacks designed to bypass safety guardrails and extract unauthorized information.
Semantic Jailbreaks
Creative attacks that circumvent content policies through context manipulation.
Inference Probing
Techniques to extract model architecture, training data, or system prompts.
RAG Exploitation
Attacks targeting retrieval-augmented generation pipelines and knowledge bases.
Agent Manipulation
Exploit agent-based workflows to execute unauthorized actions or access sensitive data.
Output Manipulation
Test insecure handling of model outputs that could lead to injection attacks.
Why AI Security Matters
AI introduces attack surfaces traditional testing cannot detect.
Prevent Prompt Injection
Identify vulnerabilities that allow attackers to manipulate AI behavior through crafted inputs.
Protect Proprietary Logic
Prevent extraction of valuable model weights, architecture, or training methodologies.
Identify Data Leakage
Find vulnerabilities that expose training data or sensitive information through model outputs.
Free Retesting
Complimentary retest of all findings within 30 days to validate remediation.
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