AI Security

As an Offensive Security engineer, I maintain a curated set of notes on AI security, adversarial testing, and red team methodologies. This section provides a structured overview of how I approach AI as an attack surface—covering vulnerabilities, threats, and offensive testing strategies.

AI Security Overview

Artificial Intelligence is increasingly embedded into critical workflows—customer service, decision support, security monitoring, and even development pipelines. With this integration comes an expanded attack surface.

This section captures how I approach AI systems as a target: mapping weaknesses, building attack chains, and evaluating real-world risks. Rather than abstract research notes, these entries are written for use in red team operations, penetration tests, and adversarial assessments.

Why AI Security Matters in Offensive Testing

AI systems inherit traditional software vulnerabilities (APIs, cloud misconfigurations, insecure endpoints) but also introduce entirely new classes of risk—from prompt injection to data poisoning.

For an attacker, this creates opportunities for:

Persistence & Pivoting – Using AI-integrated systems as footholds.
Data Exfiltration – Extracting sensitive information from models or pipelines.
Control Manipulation – Altering model behavior for fraud, misinformation, or access escalation.
Supply Chain Compromise – Poisoning dependencies, datasets, or model weights.

AI is Just Another Attack Surface

While AI feels novel, the offensive approach remains the same: reconnaissance, exploitation, persistence, and impact. The difference lies in how the vulnerabilities manifest and how they’re chained into attacks.

Modular Approach to AI Security

Just as with traditional penetration testing, I treat AI security as modular. I try to map each note to phases and objectives in red team operations:

Reconnaissance & Mapping – Identifying exposed AI systems, APIs, and data flows.
Prompt Injection & Jailbreaks – Testing controls and filters, exfiltrating hidden instructions.
Model Exploitation – Membership inference, model extraction, and training data leakage.
Adversarial ML Attacks – Data poisoning, evasion attacks, backdoored models.
AI Infrastructure & Supply Chain – Insecure vector DBs, CI/CD pipelines, model registries.
Abuse Scenarios – Fraud automation, misinformation campaigns, compliance violations.

What I Look for in AI Security Research

Not every “AI vuln” is operationally useful. I prioritize findings that matter in real-world engagements:

Exploitability – Can this be weaponized in a red team scenario?
Business Impact – Would this meaningfully affect data integrity, confidentiality, or operations?
Chaining Potential – Can this open the door to traditional exploits (SSRF, RCE, lateral movement)?
Defensive Blind Spots – Is this something security teams usually miss?

Mindset: Offensive Testing of AI

Offensive testing of AI isn’t about proving models “can be fooled.” It’s about finding the cracks that matter in real deployments:

Methodology-First Thinking – AI vulnerabilities are mapped to existing frameworks (MITRE ATLAS, OWASP AI/LLM Top 10).
Situational Awareness – Tailoring attacks to the AI’s role (customer chatbot ≠ fraud-detection AI).
Documentation & Repeatability – Structured notes ensure findings can be reproduced, escalated, and reported clearly.

Continuous Learning in AI Security

The AI security landscape evolves as quickly as the technology itself. To stay ahead, I:

Track MITRE ATLAS, OWASP AI Top 10, and academic research.
Build and refine custom payloads and toolchains for prompt injection and adversarial ML.