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AI-FOCUSED

SEC537: AI for Offensive Security

SEC536Offensive Operations, Artificial Intelligence
  • 6 Days (Instructor-Led)
  • 36 Hours
Course authored by:
Foster NethercottMick Douglas
Foster Nethercott & Mick Douglas
Register Now
SEC537: AI for Offensive Security
Course authored by:
Foster NethercottMick Douglas
Foster Nethercott & Mick Douglas
Register Now
Jump to:

Create a highly automated, self-evolving threat ecosystem by learning both sides of AI in Offensive Operations.

Course Overview

Artificial intelligence has changed offensive security in two important ways.

Security practitioners now use AI throughout offensive operations to automate and enhance traditional attack workflows. At the same time, organizations are deploying AI-enabled applications that introduce entirely new attack surfaces requiring specialized testing.

Prepare for Both Sides of Modern Offensive AI

SEC537 brings these two different AI usages together into a single place. During the first three days, you will learn how to apply AI across offensive operations, from reconnaissance and social engineering to exploit development and malware creation – Offensive AI. The last three days of the course shift focus to Adversarial AI, where you will pentest LLM-integrated applications, RAG pipelines, AI agents, model infrastructure, and related AI components using the same attack techniques criminals use against live production systems.

Through realistic labs and hands-on exercises, you will build practical experience using AI as an offensive capability while learning how to identify and exploit weaknesses in modern AI deployments. Each attack is paired with defensive context, helping you understand not only how AI systems fail, but also the architectural decisions and controls that reduce real-world risk.

Whether your organization is adopting AI or your offensive team is already using it during engagements, SEC537 prepares you for both sides of modern Offensive AI and Adversarial AI in one complementary discipline.

What You’ll Learn

  • Apply AI across the full attack lifecycle, from reconnaissance to post-exploitation
  • Engineer AI tooling for reconnaissance, exploit development, and malware creation
  • Automate vulnerability research through AI patch diffing, OSINT, and exploit generation
  • Assess LLMs, RAG pipelines, Agents, and MCP servers using real-world adversarial TTPs
  • Exploit AI-specific attack paths including prompt injection, model attacks, and agent abuse
  • Map offensive and adversarial AI TTPs to defensive controls and architectural improvements

Business Takeaways

  • Build complete AI capabilities within your offensive security program
  • Reduce risk by evaluating AI systems with the same TTPs adversaries use in production
  • Strengthen security by integrating AI offensive operations with adversarial AI testing
  • Identify architectural weaknesses before AI-enabled applications become business risks
  • Provide engineering teams with findings that improve AI security controls and system design
  • Prepare security teams to assess both traditional attack surfaces and the AI systems

Meet Your Authors

  • Slide 1 of 2
    Foster Nethercott
    Foster Nethercott

    Foster Nethercott

    Certified Instructor

    A Marine Corps veteran and founder of Fortisec, Foster Nethercott is a SANS course author and cybersecurity professional specializing in offensive operations and AI TTPs, bringing real-world experience into SEC535.

    Read more about Foster Nethercott
  • Slide 2 of 2
    Mick Douglas
    Mick Douglas

    Mick Douglas

    Principal Instructor

    Mick Douglas is a SANS Principal Instructor, cybersecurity practitioner, and community mentor with 20+ years helping defenders master detection, incident response, and OSINT—combining hands-on tools, real-world frameworks, and a knack for making hard concepts stick.

    Read more about Mick Douglas
Slide 1 of 0

Course Syllabus

Explore the course syllabus below to view the full range of topics covered in SEC537: AI for Offensive Security.

Section 1 (SEC535)Introduction to Offensive AI and Vulnerability Exploitation

Discover how threat actors leverage AI to gather intelligence and exploit vulnerabilities. Students explore advanced OSINT tools enhanced by machine learning, examine RAG-powered penetration testing workflows, and exploit vulnerabilities using AI.

Topics covered

  • Introduction to Artificial Intelligence
  • Introduction to AI Models and Capabilities
  • OSINT AI For Penetration Testers
  • Network Reconnaissance and Vulnerability Exploitation
  • Web Exploitation Using AI

Labs

  • AI-Powered Reconnaissance
  • Automated Nmap Output Analysis Using N8N and RAG
  • Metasploit Reengineered
  • AInjection: Using AI for SQL Exploits
  • AI Assisted Brute Force and Command Injection

Overview

The first day of SEC535 focuses on AI-driven reconnaissance and social engineering attacks, equipping you with the tools and techniques modern adversaries use to infiltrate organizations.

We kick off with Open Source Intelligence (OSINT) gathering using AI, leveraging powerful tools like Spiderfoot and Bbot to uncover valuable intelligence such as DNS records, employee emails, and internal phone numbers at the notional company “Meridian Systems.”

From there, we explore how an AI Pentest Assistant powered by a Retrieval-Augmented Generation (RAG) database can streamline network enumeration and optimize vulnerability discovery to enhance penetration testing workflows.

Armed with this intelligence, we will transition to exploiting the vulnerabilities using AI with Metasploit and performing SQL Injection attacks.

Section 2 (SEC535)Social Engineering Attacks

In this section, we'll explore the art of developing convincing phishing content, as well as how to supplement it with both audio and video deepfake content. We will then finish the day by performing patch diffing with AI.

Topics covered

  • Introduction to Social Engineering
  • Creating AI-Powered Phishing Emails
  • Creating Audio Deepfakes
  • Creating Image and Video Deepfakes
  • Patch Diffing

Labs

  • Building PhishGPT
  • Lure Lab: Phishing Mini-Range
  • ElevenLabs Voice Cloning
  • AI Dialed Deception: Vishing Mini-Range
  • AI-Assisted Vulnerability Discovery and Patch Analysis

Overview

In this section, we explore how AI is transforming traditional social engineering and targeted exploitation.

We start by examining the social engineering attack surface and the psychology behind manipulation, then build practical campaigns using tools like GoPhish and SET. You will develop and tune PhishGPT to generate tailored phishing emails, leverage sentiment analysis for profiling, and automate large-scale attacks through N8N workflows.

Then, we uncover how attackers create convincing audio deepfakes with platforms like ElevenLabs and Voice.ai to enable advanced vishing scenarios. From there, we step into visual deepfakes, applying face swapping, motion transfer, and lip syncing to craft realistic video lures.

Moving deeper, we shift focus to vulnerability research with AI-assisted patch diffing. You will use tools such as DeepBinDiff and ChatGPT to identify silent patches, evaluate exploitability through zero-shot techniques, and refine prompts that accelerate finding new attack paths before they are widely known.

Finally, we introduce AI-automated patch diffing, where AI-driven agents perform binary comparisons to detect security fixes, rank exploitability, and generate working exploits before vulnerabilities are publicly disclosed.

Section 3 (SEC535)Malware Development and Security Control Evasion

Explore cutting-edge AI applications in malware engineering and defense evasion. Students learn how neural networks transform malware development, grasp how attackers circumvent AI safety guardrails, demonstrate sophisticated evasion techniques powered by machine learning, and examine living-off-the-land tactics enhanced through computational intelligence.

Topics covered

  • Fundamentals of Malware
  • Writing Malware with AI
  • Hiding, Obscuring, and Trojanizing Persistent Malware
  • Agentic Malware
  • Bypassing Security Controls

Labs

  • Introduction to Writing Malware with AI
  • Advanced Malware Writing
  • Bypassing Security Controls with AI

Overview

In this section, we examine how AI is reshaping the creation and deployment of modern malware. We begin by breaking down the fundamental components of malicious software and exploring how attackers build anti-analysis capabilities to evade detection.

From there, you will use AI to develop custom malware, starting with generating proof-of-concept payloads and progressively layering in quality-of-life and stealth features. We also highlight curious, sometimes unpredictable behaviors that emerge when tools like ChatGPT assist in crafting malicious code.

Moving forward, you will learn techniques for hiding, obscuring, and embedding persistent malware using alternate data streams, wrapped execution, dynamic attribute access, and encoding substitutions. You will automate obfuscation, implement WMI persistence, build malicious DLLs, and use AI to trojanize payloads.

Finally, we cover advanced agentic malware, leveraging AI assistants and frameworks like GNAW to rewrite code in memory, deploy subordinate programs, and bypass security controls by disabling Defender, defeating tamper protection, and writing AMSI bypasses.

Section 4 (SEC536)Foundations of Attack Techniques

In this section, we establish how AI models process input, then use that understanding to locate live deployments and subvert their instructions. Coverage moves from model internals through AI-specific reconnaissance into direct and indirect injection, memory and RAG poisoning, and multimodal attacks.

Topics covered

  • How LLMs process input: tokenization, embeddings, attention, and sampling
  • Frameworks and case studies: OWASP LLM Top 10, MITRE ATLAS, EchoLeak
  • AI-specific reconnaissance, stack mapping, and model fingerprinting
  • Direct and indirect injection: prefill, role confusion, lethal trifecta, Memory and RAG poisoning, canary tokens, and multimodal evasion attacks

Labs

  • Lab 1.1: Abusing AI Assistants
  • Lab 1.2: Reconnaissance Against AI Systems
  • Lab 1.3: Prompt Injection
  • Lab 1.4: AI Memory and Context Poisoning
  • Lab 1.5: Computer Vision: Evasion and Attacks

Overview

Attacking an AI system starts with understanding why it fails.

Section 1 establishes how models process input (tokenization, embeddings, attention, and sampling) and shows how each of those mechanisms becomes an attack primitive rather than an implementation detail. You will learn why the instruction/data boundary that protects traditional applications was never designed to exist in a language model, and why safety alignment is a probability adjustment rather than a control.

From there, you move into reconnaissance. AI deployments announce themselves through exposed inference endpoints, orchestration frameworks, vector databases, model registries, and observability dashboards. You will fingerprint the full AI application stack from the outside in, identify the model behind an API, discover vision systems, and map every surface where untrusted content can reach a context window.

The section closes with direct prompt injection: attention capture, instruction override, prefill and context injection against API message arrays, ChatML and application delimiter escapes, role confusion, and function schema poisoning.

Section 5 (SEC536)Infrastructure, Integrations, and Advanced Attacks

In this section, we explore how the components surrounding AI models create their own attack surface. From safety training and guardrail evasion to infrastructure exposure, timing side channels, and API misconfiguration, this section focuses on exploiting the implementation layer: where and how AI meets the real world.

Topics covered

  • How safety training works and why jailbreaking succeeds
  • Jailbreak techniques: personas, logic traps, framing, and policy puppetry
  • Defense stack fingerprinting and classifier evasion techniques
  • Exposed inference hosts, ML platforms, and observability dashboards, Timing side channels, API authorization flaws, and weight extraction

Labs

  • Lab 2.1: Jailbreaks and Weight Theft
  • Lab 2.2: AI Architecture Flaws
  • Lab 2.3: Timing Side Channels
  • Lab 2.4: Exploiting Mass Assignments and Hidden Features
  • Lab 2.5: Model Theft and Extraction

Overview

Guardrails are behavioral, not architectural.

Section 2 is about moving the output distribution far enough that refusal stops being the most likely next token. You will work through the jailbreak technique tree: persona depth, logic traps, hypothetical framing, Bad Likert Judge, persuasion patterns, chain-of-thought corruption, policy puppetry, context flooding, positional exploitation, and attention hijacking. For each, you will learn the measured success rate behind the technique.

You then work the defense stack. Section 2 covers identifying which layer blocked you, fingerprinting commercial guardrail products, and defeating input and output classifiers through multi-language attacks, token smuggling, semantic reframing, encoding, obfuscation stacking, MathPrompt, ASCII art, and payload splitting.

The section closes on infrastructure and APIs. Exposed Ollama hosts, MLflow tracking servers, Weights and Biases keys, and observability dashboards hand over training artifacts and full prompt histories. Timing side channels recover a system prompt token by token, and logit leakage feeds direct weight extraction. Provider credential shapes, layered authorization models, recursive query loops, and mass assignment of hidden fields round out the API attack surface, including denial-of-wallet.

Section 6 (SEC536)Alignment, Interfaces, Agents, and MCP

Alignment is not a control; it is a probability adjustment, and the interfaces around the model are their own attack surface. This section covers the alignment failures you can test in a production deployment, sycophancy and constitutional AI abuse, the streaming and rendering layers that carry stored XSS and SSRF, and agentic systems where a single injection inherits every capability the agent holds.

Topics covered

  • Emergent behavior, bias, and misalignment as a testable attack surface
  • Alignment attacks: reward hacking, goal misgeneralization, and sandbagging
  • Sycophancy and constitutional AI: principle conflict, scope abuse, leakage
  • Streaming response forgery, stored XSS in AI chat, SSRF, and sandbox escape
  • Agentic attacks, multi-agent topologies, and MCP exploitation

Labs

  • Lab 3.1: AI Alignment Issues: Discovery
  • Lab 3.2: Weaponizing a Sycophantic AI
  • Lab 3.3: Stored XSS via AI Chat
  • Lab 3.4: Attacking Agentic AI
  • Lab 3.5: Attacking MCP Servers

Overview

Alignment is not a control.

Section 3 opens with emergent behavior, bias, and misalignment as a testable attack surface, then moves into the failure modes you can probe in a production deployment: specification gaming, reward hacking, goal misgeneralization, alignment faking, and sandbagging. Each one comes with a probe methodology rather than a definition. Sycophancy gets four attack patterns, and constitutional AI receives dedicated treatment through principle inference, principle conflict, scope abuse, and constitution leak and parroting.

Attention then turns to the interfaces around the model. Streaming responses arrive as server-sent event fragments rather than finished sentences, and forged events on the wire break client-side parsers. Attacker-controlled text reaches model output and persists in transcript stores, turning an AI chat into stored XSS through markdown rendering, unsafe sinks, and outbound URLs. Server-side request forgery and sandbox escape close the interface layer, including shared-volume persistence, interpreter escapes, and kernel-level escapes across Docker, gVisor, and Firecracker.

The section closes on agentic systems, where a single injection inherits every capability the agent holds. You will attack hierarchical, pipeline, swarm, and debate topologies, inject plans and manipulate goals, exploit inter-agent trust and impersonation, compromise orchestrators, and exploit MCP servers through tool manifest enumeration, tool poisoning, user interface abuse, and rug pulls.

Things You Need To Know

Important! Bring your own system configured according to these instructions!

A properly configured system is required to fully participate in this course. If you do not carefully read and follow these instructions, you will likely leave the class unsatisfied because you will not be able to participate in hands-on exercises that are essential to this course. Therefore, we strongly urge you to arrive with a system meeting all the requirements specified for the course.

It is critical that you back-up your system before class. It is also strongly advised that you do not bring a system storing any sensitive data.

CPU

  • 64-bit Intel i5/i7 2.0+ GHz processor
  • CRITICAL NOTE: Apple Silicon devices cannot perform the necessary virtualization and therefore cannot be used for this course.
  • Your system's processor must be a 64-bit Intel i5 or i7 2.0 GHz processor or higher. To verify on Windows 10 or 11, press Windows key + "I" to open Settings, then click "System", then "About". Your processor information will be listed near the bottom of the page. To verify on a Mac, click the Apple logo at the top left-hand corner of your display and then click "About this Mac".

BIOS

  • Enabled "Intel-VT"
  • Intel's VT (VT-x) hardware virtualization technology must be enabled in your system's BIOS or UEFI settings. You must be able to access your system's BIOS to enable this setting in order to complete lab exercises. If your BIOS is password-protected, you must have the password. This is absolutely required.

RAM

  • 16 GB RAM is highly recommended for the best experience. To verify on Windows 10, press Windows key + "I" to open Settings, then click "System", then "About". Your RAM information will be toward the bottom of the page. To verify on a Mac, click the Apple logo at the top left-hand corner of your display and then click "About this Mac".

Hard Drive Free Space

  • 100 GB of FREE space on the hard drive is critical to host the VMs and additional files we distribute. SSD drives are also highly recommended, as they allow virtual machines to run much faster than mechanical hard drives.

Operating System

  • Your system must be running either the latest version of Windows 10, macOS 10.15.x or later, or Linux that also can install and run VMware virtualization products described below.

Additional Software Requirements

VMware Player Install:

  • Download and install VMware Workstation Pro 17+ (for Windows hosts), or VMWare Fusion Pro 13+ (for macOS hosts) prior to class beginning. Workstation Pro and Fusion Pro are now available free for personal use from the VMware website. Licensed commercial subscriptions to these products can also be used.
  • Other virtualization products, such as Hyper-V and VirtualBox, are not supported and will not work with the course material.

Your course media will now be delivered via download. The media files for class can be large, some in the 40 - 50 GB range. You need to allow plenty of time for the download to complete. Internet connections and speed vary greatly and are dependent on many different factors. Therefore, it is not possible to give an estimate of the length of time it will take to download your materials. Please start your course media downloads as you get the link. You will need your course media immediately on the first day of class. Waiting until the night before the class starts to begin your download has a high probability of failure.

If you have additional questions about the laptop specifications, please contact customer service.

Visit this site: AI Disciplines In Offensive Operations.

SEC537 is designed for security practitioners who want to both use AI throughout offensive operations and assess the security of modern AI systems. This course is recommended for:

  • Penetration testers looking to integrate Offensive AI into their methodology while learning to assess AI-enabled applications.
  • Red team operators who want to combine AI-assisted offensive tradecraft with adversarial testing of LLMs, RAG pipelines, AI agents, and related AI systems.
  • Application security engineers and security architects responsible for evaluating the security of AI features as they move into production.
  • Security consultants conducting offensive security assessments for organizations deploying AI-enabled applications.
  • Security researchers exploring Offensive AI techniques, adversarial AI testing, and emerging attack methodologies.
  • Incident responders, SOC personnel, and cyber defenders who need to understand how AI is changing attacker capabilities and expanding the enterprise attack surface.
  • AI and machine learning engineers who want to understand how deployed models, agents, and AI infrastructure appear from an attacker's perspective.
  • Security managers, directors, and CISOs building offensive security programs that must address both AI-assisted attacks and the security of enterprise AI deployments.

Both courses in this bundle offer a GIAC certification: GOAA for SEC535, and GAIPT for SEC536. You might select to take both, just one, or none.

GIAC Offensive AI Analyst (GOAA)

The GIAC Offensive AI Analyst (GOAA) certification validates ability to apply practical, real-world offensive artificial intelligence techniques to modern cybersecurity challenges. Certified professionals prove their proficiency in applying advanced tactics such as deepfake-enabled phishing, automated vulnerability discovery, and AI-driven attack simulations to emulate sophisticated adversaries.

  • AI-powered reconnaissance and OSINT automation
  • AI-aided vulnerability discovery, patch diffing, and exploit generation
  • Malware development with AI
  • Bypassing security controls and guardrails
  • Designing and deploying AI-driven phishing
  • Legal, ethical, and OPSEC considerations

More Certification Details

GIAC AI Penetration Tester (GAIPT)

The GIAC AI Penetration Tester (GAIPT) certification validates a practitioner's hands-on AI penetration testing skills for assessing and exploiting vulnerabilities in enterprise AI systems.

  • Direct and indirect prompt injection
  • RAG exploitation
  • Agentic systems
  • Alignment problems
  • MCP server attacks
  • API security
  • AI Architectural flaws

More Certification Details

  • Unlimited access to several hands-on lab exercises that never expire
  • Printed and electronic course books and a hands-on workbook
  • MP3 audio files of the entire course
  • Detailed video walkthroughs for all lab exercises
  • Visual association maps to break down complex material
  • A digital index for quick reference to all material
  • Bonus content and hands-on exercises to develop your skills beyond the course
  • Essential cheat sheets for tools and complex analysis tasks

Students should have a solid understanding of cybersecurity fundamentals, including enterprise security architecture, web applications, and application security concepts. Experience working with HTTP APIs is recommended. A basic familiarity with AI and machine learning concepts will help students understand the technologies being assessed, but prior AI experience is not required. Python knowledge is useful for customizing offensive tooling during selected labs but is not a prerequisite. 

Start with SEC504: Hacker Tools, Techniques, and Incident Handling to build a strong offensive security foundation. Continue with SEC560: Enterprise Penetration Testing to master modern penetration testing methodology and the full attack lifecycle. From there, deepen your offensive expertise with SEC660: Advanced Penetration Testing, Exploit Writing, and Ethical Hacking, expand into SEC699: Advanced Purple Teaming, Adversary Emulation & Detection Engineering, or complement your offensive skills with FOR508: Advanced Incident Response, Threat Hunting, and Digital Forensics.

AI for Offensive Security covers two related disciplines. Offensive AI applies AI to established attack workflows, including reconnaissance, vulnerability research, social engineering, exploit development, and malware creation. Adversarial AI treats AI applications as targets, testing LLMs, RAG pipelines, agents, models, APIs, and MCP servers for weaknesses such as prompt injection, data exposure, excessive agency, model manipulation, and tool abuse.

Both disciplines now matter because AI is changing the operator and the target at the same time. Threat actors are using AI across cyber operations, while organizations are connecting AI systems to sensitive data, tools, and business processes. Traditional penetration testing remains necessary, but it was not designed to fully assess how models interpret instructions, retrieve data, use tools, or act through connected systems.

It can help differentiate you by building hands-on skills in two specialized areas: using AI across offensive operations and testing AI systems as attack surfaces. These capabilities support career growth across penetration testing, red teaming, application security, AI security, and consulting. 

Course Schedule and Pricing

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  • Location & instructor

    SANS Raleigh 2026

    Raleigh, NC, US & Virtual (live)

    Date & Time
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    Course price
    $8,780 USD*Prices exclude applicable local taxes
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