AI/LLM Red-Teaming
Adversarial security assessment of artificial intelligence systems. Probing language models, AI agents, and machine learning pipelines for vulnerabilities that traditional security testing misses.
Why AI Red-Teaming
AI is a continuation of systems-level curiosity applied to learning machines. Rather than viewing AI as separate from classical computing, it is another layer in the stack—how models are trained, deployed, interact with hardware accelerators, and how they can fail. Data leakage, model inversion, adversarial inputs, and alignment are not abstract debates but concrete engineering problems.
Attack Vectors
Prompt Injection
Crafting inputs that override system instructions, manipulate model behavior, or extract hidden prompts and system configurations.
Data Extraction & Leakage
Probing models to reveal training data, memorized content, personally identifiable information, or proprietary knowledge embedded during fine-tuning.
Model Inversion
Reconstructing training data characteristics through carefully designed queries, exposing privacy vulnerabilities in deployed models.
Adversarial Inputs
Generating inputs that cause models to produce incorrect, harmful, or manipulated outputs while appearing benign to human observers.
Alignment Failures
Studying how safety training and RLHF can be circumvented, analyzing the gap between intended behavior and actual model responses under adversarial pressure.
Tool & Agent Exploitation
Testing AI agents with tool access for unintended code execution, unauthorized API calls, and privilege escalation through chained tool invocations.
Methodology
Reconnaissance
- ▸System prompt extraction attempts
- ▸Model capability mapping
- ▸Guardrail boundary detection
- ▸Output behavior fingerprinting
Attack Surface Analysis
- ▸Input parsing vulnerabilities
- ▸Context window manipulation
- ▸Multi-turn conversation exploitation
- ▸Cross-modal attack vectors
Exploitation & Testing
- ▸Automated jailbreak generation
- ▸Embedding-level attacks
- ▸Fine-tuning poisoning experiments
- ▸Token-level adversarial perturbation
Reporting & Remediation
- ▸Structured vulnerability documentation
- ▸Reproducibility verification
- ▸Mitigation strategy proposals
- ▸Responsible disclosure coordination
Core Principles
- ▸AI models are computational structures—parameterized functions shaped by data, optimization, and constraints
- ▸Adversarial testing is concrete engineering, not abstract ethics debate
- ▸Transparency, controllability, and verifiability over opacity and blind trust
- ▸AI tools are collaborators, not authorities—augmenting human reasoning without replacing it
- ▸Deep understanding of failure modes is prerequisite for building reliable systems
Beyond Testing
The goal is not just to break AI systems but to understand their failure modes deeply enough to build better ones. AI systems should be transparent, controllable, and verifiable—not opaque and unquestioned. In AI-assisted workflows, tools are collaborators rather than authorities, augmenting human reasoning without replacing it.