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InfoSec4TC
Empowering Cybersecurity Professionals Worldwide
May 2026 — AI Security Series
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AI Security Series • Part 2 of 3
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How Hackers Poisoned an AI Model — And No One Noticed for 3 Months
Prompt injection. Training data poisoning. Adversarial images. The attack playbook is already written.
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Hi {{ first_name }},
Last week I talked about why AI Security is the fastest-growing career in cybersecurity. This week, let’s get specific.
Because here’s the thing — AI systems fail in ways that nothing in traditional cybersecurity training prepared you for.
A firewall either allows traffic or it blocks it. An AI model? You can manipulate it into doing what you want while it still looks like it’s functioning normally. And that’s what makes AI security so technically fascinating — and so dangerous when organisations get it wrong.
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The Offensive Side: How Attackers Exploit AI Systems
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Attack Vector 1
Prompt Injection
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The most common LLM attack right now. An attacker embeds hidden instructions inside user input — or even inside a document the AI is asked to read — that override the system prompt and make the model do something it wasn’t supposed to. A customer service chatbot starts leaking internal data. An AI coding assistant inserts a backdoor into the code it generates.
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Attack Vector 2
Training Data Poisoning
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This one is slower and more insidious. An attacker quietly corrupts the data used to train or fine-tune a model — introducing subtle biases, backdoors, or classification errors. By the time the model goes live, the damage is already baked in. Real incident: researchers demonstrated this against Hugging Face model repositories in 2024.
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Attack Vector 3
Adversarial Machine Learning
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A carefully crafted input — an image with imperceptible noise, a sentence with a subtle misspelling — can cause an AI model to produce completely wrong outputs with high confidence. Attackers use frameworks like TextAttack and Foolbox to generate these inputs at scale.
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Attack Vector 4
Supply Chain Attacks on ML Pipelines
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AI development relies on a chain of dependencies — model repositories, training datasets, open-source frameworks. Compromising one link in that chain can affect thousands of downstream deployments. The SAP AI Core vulnerability (2024) and the Trellix source code breach are examples of this category.
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Attack Vector 5
Excessive Agency Exploitation
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When an LLM-based agent has permissions it doesn’t need — access to files, APIs, external services — an attacker who successfully manipulates the model can leverage those permissions for real damage. This is the AI equivalent of privilege escalation.
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The Defensive Side: How AI Security Specialists Fight Back
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For every attack vector above, there’s a defensive control — and building these is where the real practitioner skill comes in:
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Layered input validation and output filtering to catch prompt injection attempts before they reach the model |
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Adversarial Robustness Toolbox (ART) for testing model resilience against adversarial inputs |
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SBOM generation and model signing to protect the ML supply chain |
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STRIDE threat modeling applied specifically to AI system architecture |
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AI-specific WAFs and guardrails that sit between the user and the model |
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Incident response playbooks designed for model compromise, not just network breach |
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This is not theoretical. These are the controls being deployed in production AI systems right now — by the organisations that are taking AI risk seriously.
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The Certification That’s Emerging to Validate All of This
Certified AI Security Professional (CAISP™)
One of the first credentials specifically designed to validate hands-on AI security expertise — covering both the red team and blue team sides of AI system security.
In the next few days, I’ll be sharing something we’ve been building for the past few months that will let you master all of this with live instruction, 40+ hands-on labs, and a cohort of security professionals from around the world.
Keep an eye on your inbox. 👀
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Stay sharp,
Dr. Mohamed Atef
Founder, InfoSec4TC
Empowering cybersecurity professionals worldwide
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🎁 Free Resources for You
Five free resources to go deeper on AI security — no registration required:
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OWASP LLM Top 10 — The definitive reference for the top 10 security vulnerabilities in LLM applications
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MITRE ATLAS — Adversarial tactics and techniques targeting AI/ML systems (MITRE ATT&CK for AI)
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CISA AI Security Guidance — Free guidance on deploying AI safely and securely in organisations
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Adversarial Robustness Toolbox (ART) — IBM’s open-source Python library for testing AI model defenses
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NIST AI Risk Management Framework — Framework for managing risks across the full AI lifecycle
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Your free cybersecurity career guide. Used by 90,000+ InfoSec4TC students worldwide.
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