Pentesting GenAI LLM models: Securing Large Language Models

Conduct penetration testing by simulating ethical attacks to uncover vulnerabilities in systems and networks, guided by Mitre Attack Framework and Monitor Alters within strict, scoped rules to patch weaknesses.

Join the milestone in pentesting GenAI LLM models and maintain top 20% status by engaging with course quality, playback controls, English subtitles, and direct Q&A for answers.

Explore why LLMs are vulnerable, including prompt injection, data leakage, and pattern-copying behavior, and learn defenses like input checks, output filters, and safety trainings.

Demonstrate that benchmarks alone do not ensure AI safety or security; performance tests miss risks like harmful content, discriminate against groups, or malicious use, calling for a broader safety framework.

Learn about key vulnerabilities in LM applications through a code walkthrough using Lang Chain tools, text loader, recursive character text splitter, Olama embeddings, chroma, and Gemma.

Exhibit inherited biases in llm applications by comparing mother and father advice, revealing gender stereotypes in responses; explains roots in training data and prompts shaping bias.

Demonstrates how llm application vulnerabilities can leak sensitive data from a knowledge base through embeddings and a structured prompt template, exposing admin credentials and other user details.

Handle service disruptions gracefully by validating long messages and simulating API timeouts to prevent crashes under stress. Enforce an 8000 character limit, pause five seconds, and return an API error.

Analyze how LLMs hallucinate convincing but false financial claims, such as fake rewards or high interest rates, and examine red-teaming and retrieval quality fixes to counter these vulnerabilities.

Compare foundation models like ChatGPT and Llama with real-world apps, and highlight the need for guardrails, filters, audits, and security checks to prevent bias, toxicity, hallucinations, privacy leaks, and misuse.

Tailor AI safety like a suit by identifying worst-case risks and applying the OWASP AI top ten with AI Incident Database to test, filter, and retrain for responsible protection.

Identify major LLM risks—bias, privacy leaks, service disruption, and hallucinations. Apply defenses through filters, stress tests, and human-led policies to protect users and data.

Explore a demo LLM application in a banking context, where a chatbot answers questions by retrieving documents and using an orchestrator to route queries, manage context, and log activity.

Import and initialize the llm application from helpers Zephyr app, then send a hello via the chat method and observe Zephyr bot respond to bias and stereotypes.

Penetration testing reveals hidden weaknesses in GenAI LLM defenses, delivering actionable risk insights, a detailed report with patches, and a roadmap for regulatory compliance and stronger defenses against AI-powered attacks.

Differentiate red teaming from penetration testing by comparing a months-long organization-wide attack simulation with a focused, checklist-driven security test, revealing both resilience gaps and actionable fixes.

Plan and reconnaissance define scope and engagement, then scan with Nmap and Nessus to uncover vulnerabilities, and perform exploits with post-exploitation and remediation, retesting to strengthen security culture.

Apply the exploitation phase of a penetration test to prove vulnerabilities can be weaponized by crafting exploits, simulating attacks, escalating privileges, and conducting impact analysis of payload injection.

Investigate the post-exploitation phase by simulating data exfiltration, establishing persistence with backdoors and commands and control infrastructures, mapping the LMS architecture, and validating cleanup to reveal long-term risks.

Explore five red-teaming techniques to bypass large language model safeguards, including prompt injection, implicit-bias prompts, context manipulation, and jailbreaking, demonstrated in a Mozart biography demo.

Detail all findings from the penetration test, including vulnerabilities and exploited weaknesses; assess risk, exploitability, and impact; provide actionable recommendations and an executive summary for CIOs, CFOs, and CEOs.

Document all findings and exploits, perform a contextual risk and impact analysis, and provide actionable remediation recommendations with continuous monitoring support and a final reflective review.

Explore the MediaTek framework, a Mitre-led guide to attacker tactics, techniques, and procedures (TTPs) that informs threat-informed defenses and defense planning, with Microsoft Sentinel integration.

Explore the pyramid of pain to prioritize defenses around TTPs, then monitor tools, network host artifacts, domain names, IP addresses, and hash values to disrupt attacks.

Explore MITRE ATT&CK matrices across enterprise, mobile, and ICS, with enterprise submatrices for Windows, Linux, Mac OS, and cloud services such as Azure, Android, Office 365, and Google Workspace.

Explore the Mitre ATT&CK tactics, a 14-stage framework from reconnaissance to impact, detailing how attackers plan, access, execute, persist, evade defense, and exfiltrate data.

Explore how attackers translate tactics into actionable techniques, from active scanning and resource development to persistence, defense evasion, credential access, and data exfiltration.

Explore how ATT&CK subtechniques add detail to attack plans, with examples across reconnaissance, initial access, persistence, lateral movement, and exfiltration, including phishing, DLL injection, and RDP.

Explore data sources as the raw telemetry you collect to detect attackers' reconnaissance, active scanning, and vulnerability scanning within the MITRE ATT&CK framework, including network traffic and security logs.

Implement detection strategies by turning raw data into actionable alerts. Analyze logs to spot reconnaissance and scanning patterns, triggering timely alerts to counter attacker techniques.

Implement mitigations as proactive defenses that reduce attack paths, harden defenses like web application firewall log, and enforce robust privileged account management to prevent escalation.

Learn how threat groups are defined by shared behaviors and goals, and how vendors name the same attackers differently, such as Apt41 with names like midnight blizzard.

Explore how software, including tools and malware, links to specific ATT&CK techniques, groups, and campaigns, and learn how built-in tools like PowerShell are misused to enable attacks and defend systems.

Identify campaigns as coordinated, multi-target operations in the Mitre attack framework, carried out over a defined period with a unified goal by nation-state actors or sophisticated cyber criminals.

Explore how adversary groups, tactics, techniques, sub-techniques, and software form campaigns, and how data sources and detections reveal an attacker playbook for securing large language models.

Explore the MITRE ATT&CK enterprise matrix in a hands-on browser demo, navigating 14 tactics, techniques, and sub-techniques, and examining mitigations, detections, data sources, and CTI context.

Examine prompt injection, a sneaky technique that tricks AI into revealing data or bypassing safety rules, with examples in chatbots, code generators, and other AI tools.

Explore how indirect prompt injection can manipulate a language model in a live chat lab to delete user accounts and reveal backend vulnerabilities in an ecommerce-like setting.

Explore insecure output handling as a key AI security risk where unsanitized outputs reach users or systems and run malicious code or links.

Learn to test insecure output handling in ai systems by using indirect prompt injection to trigger cross-site scripting, demonstrating how an xss payload can delete a user account.

Identify and defend against supply chain vulnerabilities in software by vetting vendors, verifying updates, and monitoring for red flags across open source libraries, third party APIs, and cloud services.

Learn how model denial of service threatens large language models by draining resources and degrading service quality through complex computations and endless data retrieval loops.

Defend large language models from denial of service with rate limiting, input validation, auto scaling, and lean optimization using pruning, quantization, or distilbert, plus regional redundancy and backup models.

Learn how training data poisoning introduces vulnerabilities, backdoors, and biases into ai systems, with real-world examples like tampered stop sign images and financial recognition errors, and why securing data matters.

Prevent sensitive information disclosure in large language models by securing configuration, encryption, and access control, and teaching users to pause before sharing data.

Understand how plugins extend large language models and reveal security risks like data leakage, remote code execution, and privilege escalation, caused by inadequate access controls and weak monitoring.

Explore excessive agency in AI, where systems act autonomously beyond programmer intent, and assess ethical, safety, and legal implications for essential uses. Learn to anticipate unpredictability and manage autonomous behavior.

Protect against overreliance on large language models by verifying AI suggestions, especially in legal document drafting and medical diagnostics, to prevent mistakes and misinformation.

Protect proprietary llms from unauthorized access and model theft by enforcing access control and encryption, plus policies, training, and audits to safeguard intellectual property and revenue.

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