Adversarial AI Red-Teaming & Safety Auditing Python LLMs + MATLAB/Simulink Verification

Harden Your AI Against Jailbreaks, Prompt Injection & Safety-Critical Failure

We provide dual-discipline AI defense: advanced Python adversarial red-teaming (multi-turn jailbreaks, RAG exfiltration, automated fuzzing with garak & PyRIT) combined with MATLAB/Simulink formal verification (reachability analysis, barrier certificates, ISO 26262 / DO-178C certification) for mission-critical autonomous systems.

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OWASP Top 10 for LLM
Formal Mathematical Proofs
Actionable Remediation Code

Vulnerabilities We Neutralize

Direct & Indirect Prompt Injections Malicious prompt payloads embedded inside ingested PDFs, scraped websites, or user inputs that hijack system instructions.
Vector DB & RAG Exfiltration Adversaries crafting semantic queries that force retrieval of confidential documents or customer PII from vector embeddings.
Autonomous Control Boundary Violations Neural network controllers commanding unsafe torque, steering, or voltage outside physical stability boundaries under unseen edge cases.
Tool Misuse & Unconstrained Function Calling Autonomous LLM agents executing unauthorized database drops, unvetted API calls, or privilege escalation.
Our Dual-Stack Advantage

Software LLM Red-Teaming Meets Safety-Critical Engineering

Most security firms only test web chatbots. We bridge modern cloud AI with embedded, safety-critical aerospace and automotive systems.

Track A: Software & Generative AI Python Ecosystem

Adversarial LLM & Agent Red-Teaming

Rigorous adversarial testing using industry standard attack frameworks to expose security vulnerabilities before malicious actors exploit them in production.

Automated Fuzzing & Scanning: Continuous vulnerability probes utilizing garak, Microsoft PyRIT, and custom multi-turn Crescendo jailbreak suites.
PII Masking & Privacy Leakage Audits: Testing memory extraction and deploying Microsoft Presidio anonymizers to scrub sensitive entities in real time.
Production Guardrail Deployment: Integrating NVIDIA NeMo Guardrails and Meta Llama Guard 3 to enforce input/output rails and deterministic JSON schemas.
Track B: Embedded & Physical AI MATLAB / Simulink

Formal Verification for Safety-Critical Systems

Mathematical certification that neural networks controlling electric vehicles, drones, surgical robots, or microgrids will never exceed safe dynamic limits.

Deep Learning Toolbox Verification Library: Formal reachability analysis computing reachable output sets to guarantee zero state violations under bounded input noise.
Deterministic Barrier Supervisors: Designing Control Barrier Function (CBF) supervisor blocks in Simulink that instantly override AI outputs if physical envelopes are breached.
Regulatory Certification Support: Preparing mathematical verification evidence aligned with ISO 26262 (ASIL-D), DO-178C / DO-331, and IEC 62304 standards.
Live Threat Interception

Simulated Attack vs. Guardrailed Response

See firsthand how unprotected AI fails and how our engineering safeguards neutralize attacks in real time.

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Transparent Engineering Rates

AI Red-Teaming & Safety Audit Packages

Rigorous testing methodologies delivering comprehensive vulnerability logs, reproduction scripts, and defense code.

LLM Pentest & Scan

Rapid automated adversarial scan for consumer or internal chatbots and RAG apps.

$1,500 / audit
  • 500+ Automated test probes via garak
  • Direct prompt injection & system prompt leak audit
  • OWASP Top 10 for LLM compliance summary
  • Step-by-step developer remediation report
  • Live production guardrail implementation
Book LLM Pentest

Full Red-Team & Guardrails

Complete adversarial engagement with human-in-the-loop multi-turn attacks and custom defense deployment.

$2,900 / engagement
  • Everything in LLM Pentest, plus:
  • Multi-turn human-in-the-loop Crescendo attacks
  • RAG poisoning & indirect injection evaluation
  • Custom NeMo Guardrails or Llama Guard setup
  • Real-time Microsoft Presidio PII redaction code
  • Post-remediation verification re-test
Book Full Red-Team

Safety-Critical Formal Verification

For autonomous vehicles, UAVs, robotics, and medical devices running neural network controllers.

$4,800 / system
  • Formal reachability analysis in MATLAB
  • Input-output perturbation bound proofs
  • Simulink Barrier Certificate Supervisor Block
  • ISO 26262 (ASIL-D) / DO-178C evidence package
  • 1-on-1 PhD Control Systems Engineer Support
Request Formal Verification
Live 2-Weekend Zoom Cohort

Master Technical AI Red-Teaming & Safety Verification

Want to train your engineering and ML teams to break and secure AI models themselves? Enroll in our live interactive Zoom masterclass featuring live attacks with garak and formal proofs in MATLAB.

4 Intensive Sessions (12 Hours)
Live Sandbox Attack Demos
Professional Credential Included
View Syllabus & Cohort Dates →
Frequently Asked Questions

AI Safety & Red-Teaming FAQs

Traditional pentesting looks for deterministic deterministic software bugs (SQL injection, XSS, CSRF, broken authentication). AI red-teaming investigates non-deterministic, probabilistic machine learning risks: jailbreaks that manipulate semantic latent space, indirect prompt injection inside ingested documents, toxic hallucinations, training data leakage, and unconstrained agent tool calls.

In Python, we leverage garak (LLM vulnerability scanner), Microsoft PyRIT (Python Risk Identification Toolkit for generative AI), Meta Llama Guard 3, NVIDIA NeMo Guardrails, and Microsoft Presidio for PII redaction. In MATLAB/Simulink, we employ the Deep Learning Toolbox Verification Library, Simulink Design Verifier, and custom barrier certificate solvers.

We perform formal reachability analysis: defining an input perturbation hypercube (e.g., maximum possible sensor noise or adversarial camera distortion) and mathematically propagating it through every layer of the trained neural network. This computes the exact bounding polyhedra of all possible outputs, proving whether the network can ever command an unsafe state without needing to run infinite random simulations.

Yes. We perform black-box red-teaming against customer endpoints, custom GPTs, and RAG pipelines via external API querying, testing prompt injection, retrieval poisoning, and system extraction without needing model weights or training code.

Launching a Customer-Facing LLM or Autonomous System?

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Consult Red-Team Lead