A tailored course, built for your situation
Mastering OWASP for ML Compiler Engineers
A complete system for securing AI/ML pipelines at scale
$199 one-time
24-hour access provisioning
30-day money-back guarantee
Hand-built implementation playbook
12 modules. 12 chapters per module. 144 chapters total.
12 modules, each with 12 chapters (144 chapters total), text-based, plus downloadable templates and a hand-built implementation playbook delivered alongside course access.
Security rework cycles slowing down AI toolchain releases
The situation this course is for
ML compiler updates face repeated security validation loops, delaying deployment and increasing cross-team friction during integration.
Who this is for
Senior ML infrastructure engineer working on compiler-level tooling in a high-velocity AI environment
Who this is not for
Developers focused only on application-layer security, compliance auditors, non-technical risk officers
What you walk away with
- Anticipate security review requirements during design phase, not after implementation
- Produce compiler artifacts with embedded OWASP alignment without rework loops
- Become the internal reference for secure AI/ML toolchain patterns
- Shorten pre-release validation cycles by aligning with security frameworks early
- Reduce cross-team chasing during audit and compliance review windows
The 12 modules (with all 144 chapters)
Module 1. OWASP’s Role in Modern ML Compiler Design
Understand how OWASP principles apply specifically to compiler-level AI toolchains, not just web apps or APIs. Map core threats to low-level code generation and optimization phases.
12 chapters in this module
- How OWASP’s Top 10 applies to AI-driven compiler outputs
- Threat modeling for JIT-compiled ML workloads
- Security vs. performance tradeoffs in optimization passes
- Common injection vectors in graph-based compilation
- Compiler-as-attack-surface in distributed training
- Real-world cases of exploited compiler weaknesses
- Mapping OWASP ASVS to ML pipeline stages
- When traditional appsec reviews miss compiler flaws
- Security debt accumulation in model-compilation layers
- Embedding security checks in dialect transitions
- Compiler-level side-channel risks in AI workloads
- Integrating OWASP guidance into IR validation
Module 2. AI Compiler Architectures and Security Boundaries
Break down the components of modern ML compilers and identify where security controls must be enforced at dialect, pass, and execution levels.
12 chapters in this module
- Understanding MLIR and its security surface
- Securing dialect conversion pipelines
- Trust boundaries in multi-stage compilation
- Pass scheduling and unintended side effects
- Execution engine sandboxing techniques
- Memory safety in generated kernels
- Linking security to compiler optimization levels
- Metadata handling in serialized IR
- Firmware-level implications of compiler output
- Cross-compilation attack surface
- Compiler plugin security models
- Hardening compiler-generated dispatch logic
Module 3. OWASP Integration in Compiler Development Workflow
Embed security validation directly into the development, testing, and CI/CD pipeline for ML compilers to prevent late-cycle rework.
12 chapters in this module
- Integrating OWASP ZAP into compiler test suites
- Static analysis for dialect-specific vulnerabilities
- Automated security linting in pull requests
- Security gates in CI for IR transformations
- Fuzzing compiler frontends using OWASP rules
- Generating audit-ready evidence from tests
- Threat modeling during feature planning
- Security-aware diff highlighting in code review
- Compiler change impact on security posture
- Versioned security baselines for compiler builds
- Security tagging for optimization passes
- Automated compliance reporting from CI logs
Module 4. Securing Intermediate Representations (IR)
Protect the core abstraction layer in ML compilers by hardening IR structure, validation, and transformation logic against injection and misuse.
12 chapters in this module
- Common IR vulnerabilities in ML compilers
- Sanitizing input graphs before lowering
- Validation strategies for custom dialects
- Preventing unintended side effects in passes
- IR mutation attacks and detection
- Type system weaknesses in IR design
- Embedding security metadata in IR nodes
- Ownership and lifetime checks in IR
- Cross-dialect injection risks
- IR-level sandboxing for untrusted input
- Fuzzing IR parsers for robustness
- Hardening serialization formats for IR
Module 5. Compiler Pass Design with Security in Mind
Design optimization and lowering passes with security outcomes as a first-class requirement, not an afterthought.
12 chapters in this module
- Security implications of dead code elimination
- Protecting against information leakage in optimizations
- Pass-specific attack surface mapping
- Sanitizing control flow during lowering
- Securing loop unrolling and vectorization
- Memory layout risks in tensor transformations
- Attacker exploitation of pass ordering
- Fuzzing pass logic with malicious inputs
- Hardening pass fusion decisions
- Security-aware cost modeling in scheduling
- Compiler-time vs. runtime security tradeoffs
- Auditable decision trails in pass execution
Module 6. Runtime Safety in Generated Code
Ensure that the final output of the compiler enforces memory, type, and execution safety to prevent runtime exploits.
12 chapters in this module
- Buffer overflow risks in generated kernels
- Type confusion in dynamic dispatch code
- Mitigating side-channel leaks in codegen
- Secure memory allocation patterns
- Preventing use-after-free in compiled modules
- Runtime checks vs. compile-time guarantees
- Hardening exception handling paths
- Securing inter-op function calls
- Input validation in generated inference code
- Compiler-generated defenses against ROP
- Sandboxing generated code at runtime
- Secure defaults in auto-generated wrappers
Module 7. Threat Modeling for ML Compiler Pipelines
Apply structured threat modeling to ML compiler workflows, identifying risks across input ingestion, transformation, and output stages.
12 chapters in this module
- Defining trust boundaries in compilation flow
- Identifying data flow risks in lowering passes
- Modeling attacker access to compiler inputs
- Abuse cases for malicious model submission
- Escalation paths via plugin architecture
- Threats to compiler build integrity
- Abusing optimization for denial-of-service
- Model-to-model attack propagation
- Compiler-level backdoor insertion
- Privilege escalation in cross-compilation
- Data exfiltration via compile-time side channels
- Model poisoning via IR manipulation
Module 8. Secure Handling of Compiler Inputs and Plugins
Control the risks introduced by external models, plugins, and configuration files that feed into the compiler.
12 chapters in this module
- Validating model formats for malicious content
- Sandboxing untrusted model parsers
- Plugin authentication and integrity checks
- Securing configuration file parsing
- Preventing DLL preloading in toolchains
- Plugin permission models
- Model metadata sanitization
- Compiler-level input timeout enforcement
- Mitigating memory exhaustion attacks
- Secure plugin update mechanisms
- Input-based resource exhaustion patterns
- Compiler protection against malformed inputs
Module 9. Compiler Integration with Secure Toolchains
Ensure the compiler works safely within larger AI/ML infrastructure, including model registries, serving runtimes, and monitoring systems.
12 chapters in this module
- Secure communication with model servers
- Integrity checks for model downloads
- Compiler-to-runtime trust boundaries
- Securing compiler access to accelerators
- Authentication for distributed compilation
- Hardening logs and telemetry output
- Compiler output compatibility with sandboxing
- Monitoring for anomalous compilation patterns
- Compiler integration with policy engines
- Securing shared memory in compilation clusters
- Network-level protections for remote compilation
- Compiler role in zero-trust environments
Module 10. Automating Security Validation for Compiler Releases
Build self-validating test suites and compliance checks that reduce reliance on manual review and accelerate release cycles.
12 chapters in this module
- Designing security conformance test suites
- Automated OWASP compliance scoring
- Fuzzing pipelines for regression detection
- Security benchmarking across versions
- Pre-release security gates
- Generating audit trails automatically
- Compiler self-assessment frameworks
- Versioned security baselines
- Automated vulnerability scanning in build
- Security regression detection
- Compiler integrity verification at deploy
- Zero-touch validation for patch releases
Module 11. Documentation and Knowledge Transfer for Security Practices
Create reusable documentation and onboarding materials that make security practices durable across teams and rotations.
12 chapters in this module
- Documenting security design decisions
- Creating security-aware onboarding guides
- Maintaining living threat models
- Versioning security documentation
- Security decision logs for compiler changes
- Knowledge transfer for compiler maintainers
- Embedding security guidance in code comments
- Internal training modules for new hires
- Security playbooks for incident response
- Compiler security FAQ repository
- Lessons learned from past security incidents
- Standardizing security review checklists
Module 12. Leading Security Adoption Across AI Infrastructure Teams
Become the trusted internal resource for secure compiler practices and influence broader adoption across AI engineering.
12 chapters in this module
- Building credibility on security topics
- Presenting security tradeoffs objectively
- Mentoring peers on secure patterns
- Influencing design without authority
- Creating reusable security templates
- Running internal security workshops
- Contributing to cross-team standards
- Publishing internal best practices
- Earning recognition as a security reference
- Balancing velocity and security rigor
- Scaling impact through documentation
- Measuring adoption of secure practices
How this maps to your situation
- ML compiler development at scale
- Security validation under release pressure
- Cross-team coordination on AI tooling
- Maintaining speed without sacrificing security
Before vs. after
Before
Security feedback comes late, requiring rework and slowing down releases.
After
Security decisions are made early, reducing friction and making releases predictable.
What's included with your purchase
- 12 modules with 12 chapters each (144 chapters)
- Downloadable templates and worked examples for every module
- Hand-built implementation playbook delivered alongside course access
- 30-day money-back guarantee
Delivery and format
- Course and learning environment access provisioned within 24 hours of purchase
- Hand-built implementation playbook delivered alongside course access
Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.
Time investment: Approximately 90 minutes of focused reading, plus optional deep dives and template customization.
If nothing changes
Without structured security integration, ML compilers will continue to face last-minute review delays, increasing the chance of deployment blockers and undermining trust in AI infrastructure teams.
How this compares to the alternatives
Unlike generic appsec courses, this course focuses specifically on ML compiler vulnerabilities and OWASP integration at the infrastructure level , not web apps or APIs.
Frequently asked
Is this course about web application security?
No. It focuses exclusively on applying OWASP principles to ML compiler design and AI/ML infrastructure, not traditional web apps.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me pass a certification?
It’s not designed for exam prep, but it deeply aligns with OWASP and secure systems engineering principles used in advanced roles.
$199 one-time. Approximately 90 minutes of focused reading, plus optional deep dives and template customization..
Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.
30-day money-back guarantee·
144 chapters·
Hand-built playbook included·
Account access within 24 hours