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

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

AI Safety Audit Overview: MATLABSolutions delivers full-lifecycle adversarial AI security evaluations spanning cloud generative AI and physical cyber-physical systems. We combine automated fuzzing (garak, Microsoft PyRIT), multi-turn Crescendo jailbreak testing, and Simulink formal verification for autonomous control systems (ISO 26262 ASIL-D / DO-178C). Engagements deliver reproduction scripts, defense code, and executive sign-off within 3 to 7 business days.

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

Request Red-Team Audit Chat with Red-Team Lead
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, automotive, and MATLAB simulation 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 network controllers for 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 dynamic systems 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.
Comparative Framework

AI Red-Teaming vs. Safety-Critical Formal Verification

A side-by-side technical breakdown of attack vectors, testing tools, and remediation standards across our two tracks.

Vulnerability Category Attack Vector / Failure Mode Audit & Fuzzing Tooling Engineering Defense & Standard
Prompt Injection (Direct & Indirect) Payloads concealed in ingested PDFs, Web RAG embeddings, or API prompts overriding system rules. garak automated probes, Microsoft PyRIT, bespoke multi-turn Crescendo scripts. NVIDIA NeMo Guardrails, Meta Llama Guard 3, OWASP LLM01 compliance.
Vector DB & PII Exfiltration Adversarial semantic prompts triggering retrieval of private training records, employee PII, or API keys. Targeted embedding perturbation, latent memory extraction fuzzers. Microsoft Presidio entity anonymization, tenant-isolated vector filtering, GDPR / HIPAA compliance.
Physical Control Boundary Violation Sensor noise or optical distortions commanding unsafe torque, altitude, or voltage out-of-bounds. MATLAB Deep Learning Toolbox Verification Library, reachability polyhedra computation. Simulink Control Barrier Function (CBF) QP-filter, ISO 26262 ASIL-D, DO-178C DAL-A.
Excessive Agency & Unvetted Tools Autonomous AI agents invoking unconstrained database modifications, API execution, or unauthorized code. Agent tool invocation fuzzing, parameter injection test harnesses. Strict deterministic Pydantic JSON schemas, dual-key human approval gating, OWASP LLM06.
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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Lead Auditor & Engineering Credentials

Conducted by PhD Control Engineers & Cybersecurity Specialists

Our safety verification team is spearheaded by engineering PhDs with over a decade of domain expertise in robust control theory, cyber-physical defense, and machine learning security. We do not employ junior script-runners; every engagement is led by specialists accredited in:

MathWorks Toolchain Specialists: Deep Learning Toolbox Verification, Simulink Design Verifier, and Polyspace formal methods.
OWASP GenAI Committee Alignment: Rigorous methodologies mapped to OWASP LLM Top 10 and NIST AI RMF 1.0 guidelines.
Aerospace & Automotive Safety: Hands-on verification track record for ISO 26262 (ASIL-D) and DO-178C / DO-331 airborne systems.
100% Actionable Code Fixes: Delivering production-ready NeMo rails, Presidio scrubbers, and Simulink CBF supervisory blocks.

Enterprise Confidentiality & NDA Guarantee

We recognize the profound sensitivity of proprietary models, training datasets, and RAG knowledge stores. Every client engagement is protected under strict governance:

  • Mutual Bilateral NDA: Executed prior to reviewing any architecture or endpoint.
  • Zero Model Training: Client prompts and proprietary weights are never cached, retained, or used for model training.
  • Isolated Test Sandboxes: Air-gapped fuzzing environments with complete data purging post-audit.
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 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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