Certified LLM Security & Red Team Analyst (CLLM-SRT)

Certified LLM Security & Red Team Analyst (CLLM-SRT) Certification Program by Tonex

The CLLM-SRT program develops specialists who can attack and defend language-model systems with discipline. You learn how LLMs fail under pressure, how to probe them safely, and how to build guardrails that hold in production. The unique edge is a dual focus: offensive tradecraft to reveal real risk, and defensive engineering to close it fast. We cover prompt injection, jailbreaks, covert exfiltration, and policy evasion.

You also practice designing controls for fine-tuning, retrieval-augmented generation, and tool use. The course maps work to OWASP Top 10 for LLMs and MITRE ATLAS so teams can report risks in a common language. Outcomes are practical: lower breach likelihood, reduced data leakage, and higher trust in AI features. Graduates leave with a playbook for secure model lifecycle, actionable detection patterns, and assurance methods that scale.

Learning Objectives:

• Identify and model LLM attack surfaces across apps and pipelines
• Execute and document prompt-injection and jailbreak techniques
• Design input/output controls and guardrails that withstand abuse
• Harden fine-tuning and RAG against poisoning and leakage
• Build monitoring, detection, and incident response for LLMs
• Align controls to OWASP Top 10 for LLMs and MITRE ATLAS
• Evaluate LLM security with red-team tests and benchmarks
• Communicate risk to stakeholders with clear metrics and evidence

Audience:

• Cybersecurity Professionals
• Red and purple team members
• AI/ML engineers and architects
• Security architects and AppSec leads
• SOC analysts and detection engineers
• Risk, compliance, and governance officers
• Product and platform managers for AI
• Prompt engineers and solutions engineers

Program Modules:
Module 1: LLM Security Foundations

• LLM threat landscape and attacker mindsets
• Trust boundaries in model-centric systems
• Risk taxonomy: data, model, pipeline, user
• Safety versus security tradeoffs
• Secure prompt and output channels
• Mapping to OWASP Top 10 for LLMs

Module 2: Offensive Techniques — Prompt Injection & Jailbreaks

• Direct and indirect injection patterns
• Jailbreak design and bypass heuristics
• Role and tool-use abuse strategies
• Covert data exfiltration via outputs
• Content-policy evasion tactics
• Detection signals and containment steps

Module 3: Defensive Guardrails & Policy Enforcement

• System-prompt hardening practices
• Input validation and sanitization
• Output filtering and structured constraints
• Tool-use scoping and least privilege
• Safety policies and refusal tuning
• Red-team feedback to blue-team loops

Module 4: Secure Fine-Tuning & RAG Hardening

• Data curation and PII minimization
• Dataset poisoning threats and defenses
• Fine-tuning safety alignment checks
• RAG retrieval isolation and allowlists
• Prompt templates with context controls
• Leakage evaluation across RAG chains

Module 5: Detection, Monitoring & Incident Response

• Telemetry for prompts, tools, and outputs
• Attack-pattern analytics and heuristics
• Canary prompts and deception signals
• Alerting, triage, and playbooks
• Post-incident review and lessons learned
• MITRE ATLAS technique mapping

Module 6: Validation, Benchmarking & Assurance

• Test harnesses for red-team scenarios
• Adversarial evaluation methodologies
• Benchmarking with OWASP and custom suites
• Model card and risk register updates
• Release-gate readiness criteria
• Governance, legal, and ethics checkpoints

Exam Domains:

1. LLM Threat Modeling & Architecture Risk
2. Adversarial Promptcraft & Exploitation Methods
3. Data Governance, Privacy & Training Security
4. Detection Engineering & Incident Handling for LLMs
5. Policy, Compliance & Responsible AI Governance
6. Evaluation Frameworks, Benchmarks & Assurance

Course Delivery:
The course is delivered through a combination of lectures, interactive discussions, workshops, and project-based learning led by experts in LLM security. Participants access online resources, curated readings, case studies, and tools for practical exercises.

Assessment and Certification:
Participants are assessed through quizzes, assignments, and a capstone project. Upon successful completion, participants receive the Certified LLM Security & Red Team Analyst (CLLM-SRT) certificate from Tonex.

Question Types:

• Multiple Choice Questions (MCQs)
• Scenario-based Questions

Passing Criteria:
To pass the Certified LLM Security & Red Team Analyst (CLLM-SRT) Certification Training exam, candidates must achieve a score of 70% or higher.

Ready to raise your LLM security posture? Enroll your team or request a private cohort. Let’s build trustworthy AI together.