Here’s something that might surprise you: while everyone’s worried about AI taking their jobs, the real threat might be what’s happening to the AI itself.
Think about it—we’ve spent years fortifying our digital infrastructure against hackers. We’ve built firewalls, deployed encryption, and trained entire teams in cybersecurity. But now we’re integrating AI systems into our most critical operations, and these systems come with a whole new playbook of vulnerabilities that most security teams have never encountered.
According to a comprehensive 2024 report from Booz Allen Hamilton, AI systems face five distinct attack vectors that could undermine everything from financial trading algorithms to healthcare diagnostics. And here’s the kicker: traditional cybersecurity approaches often can’t detect these attacks because AI, by design, behaves unpredictably.
The Invisible Poison
Let’s start with data poisoning—arguably the most insidious threat on the list. Imagine you’re training an AI model to detect fraudulent financial transactions. An adversary doesn’t need to hack your servers; they just need to subtly manipulate your training data. Add some noise here, change a few labels there, or inject carefully selected false data points.
The result? Your fraud detection AI now has blind spots—exactly where the attackers want them. In one documented case, two individuals fooled Shanghai’s facial authentication system and stole $77 million from the tax system. The model wasn’t broken; it was taught to see what the attackers wanted it to see.
Remediation:
Organizations must implement data integrity checks throughout the machine learning operations lifecycle, using cryptographic signatures to ensure only “original” data is used for training and evaluation.
Malware’s New Home
Here’s something most organizations don’t realize: AI models can harbor malware just like any other file. When you download that pretrained language model from the internet—perhaps as a foundation for your company’s custom AI assistant—you might be importing more than just algorithms.
Malicious code can be embedded within model file formats, some of which are inherently vulnerable. The Pytorch-nightly compromise exposed sensitive information on Linux machines through a malicious dependency package. Traditional virus scanners won’t catch these threats because they’re not designed for AI-specific file formats.
Remediation:
Model-specific scanning tools that can inspect AI files for unexpected patterns before deployment. It’s a low-cost addition to your MLOps pipeline that could save you from a catastrophic breach.
The Sticker That Stops Traffic
Model evasion attacks sound like science fiction but are remarkably simple to execute. Place a carefully designed sticker on a stop sign—an almost imperceptible change to the human eye—and an autonomous vehicle’s AI might interpret it as a speed limit sign and drive right through the intersection.
This isn’t hypothetical. Researchers have demonstrated that adversaries can engineer inputs to not just cause random misclassifications, but ensure the model sees exactly what they want it to see. The implications for autonomous systems, medical imaging AI, or security screening algorithms are profound.
Remediation:
Training models to be robust against perturbed inputs and teaching them to recognize and handle such manipulations—essentially making your AI more skeptical and context-aware.
Jailbreaking the Corporate Brain
LLM-powered chatbots are becoming the interface between employees and enterprise systems. But these models, trained on the vast expanses of the internet, can be manipulated to bypass their safety controls through a technique called jailbreaking.
Skeleton Key, a sophisticated attack, uses a multi-step process to circumvent security protocols across multiple GenAI models—including those from OpenAI, Anthropic, and Llama. Even more concerning is the “transferability” property: an attack proven against one model often works against similar models.
When ChatGPT leaked sensitive training data after being asked to simply repeat the word “poem,” it demonstrated how these systems can inadvertently reveal information they shouldn’t.
Remediation:
Organizations need input validation to filter harmful prompts, query monitoring to block suspicious patterns, and rigorous output assessment to prevent information leakage.
The Theft You Can’t See
Data leakage and model theft represent a different category of threat—one focused on stealing intellectual property rather than disrupting operations. The training data used for AI models often contains the most sensitive information an organization possesses: customer data, proprietary research, strategic insights.
Attackers use “model inversion” techniques to extract this training data, or they steal the model itself to avoid the costs of data collection and training—and to craft better attacks against the target system.
Remediation:
Differential privacy measures provide a statistical framework that anonymizes individual data while still allowing for analysis and model training. It’s not perfect, but it significantly raises the bar for would-be thieves.
What Should You Actually Do?
Here’s the reality check: there’s no one-size-fits-all solution. A comprehensive AI security strategy requires a risk-based approach tailored to your specific use cases. But every organization should start with these fundamentals:
- Risk Modeling: Identify and quantify the probability and impact of different attack scenarios specific to your AI implementations.
- Red Teaming: Simulate realistic attack scenarios to uncover vulnerabilities before adversaries do.
- Governance Protocols: Establish clear procedures, guidelines, and best practices that align with your machine learning operations lifecycle.
- Continuous Monitoring: Implement controls that monitor and manipulate model inputs and outputs, watching for signs of prompt injection or jailbreaking attempts.
- Security Testing: Quantify your model’s robustness to adversarial attacks and the likelihood that training data has been poisoned.
The maturity level of your AI implementation should guide your security investments. A standalone commercial GenAI offering like Microsoft Copilot requires different protections than a homegrown model trained on proprietary data. Third-party model integrations fall somewhere in between.
The Talent Gap Nobody’s Talking About
Here’s the final surprise: the biggest barrier to AI security isn’t technology—it’s people. The 2024 ISC2 Cybersecurity Workforce Study identified AI as the number-one security skill gap among over 15,000 cybersecurity practitioners.
Ben Aung, chief risk officer at Sage, told the Wall Street Journal that finding professionals who understand LLM security risks and can collaborate effectively with data scientists and AI engineers “is a much smaller and rarer group of people.”
This is why leading organizations are creating centralized AI security engineering teams—not to replace traditional cybersecurity, but to augment it with specialized expertise that can peer into the black box of AI operations and identify risks that conventional approaches would miss.
The Bottom Line
AI security isn’t just a technical problem—it’s an organizational imperative that requires coordination across CIOs, CISOs, CTOs, and chief risk officers. The threats are real, documented, and growing more sophisticated.
But here’s the good news: the security community is rising to meet this challenge. A vibrant ecosystem of frameworks, tools, and resources has emerged to help organizations protect their AI systems. From MITRE ATLAS’s knowledge base of adversary tactics to NIST’s AI Risk Management Framework, from Google’s Secure AI Framework to the OWASP Top 10 for LLM Applications—the building blocks for robust AI security are available.
What’s needed now is the will to implement them, the resources to staff the effort, and the organizational structure to ensure AI security becomes everyone’s responsibility.
Because in the AI era, the question isn’t whether your systems will be targeted—it’s whether you’ll be ready when they are.
Sources
- Booz Allen Hamilton (December 2024). “Securing AI: Key Risks, Threats, and Countermeasures for Enterprise Resilience.” https://www.boozallen.com/content/dam/home/docs/ai/securing-ai.pdf
- MITRE ATLAS (Adversarial Threat Landscape for AI Systems). https://atlas.mitre.org/
- NIST, “Artificial Intelligence Risk Management Framework (AI RMF 1.0)” https://www.nist.gov/itl/ai-risk-management-framework
- NIST, “Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations (NIST AI 100-2e2023).” https://csrc.nist.gov/publications/detail/nistir/8269/final
- Google, “Secure AI Framework (SAIF).” https://safety.google/cybersecurity-advancements/saif/
- OWASP Foundation, “OWASP Top 10 for Large Language Model Applications.” https://owasp.org/www-project-top-10-for-large-language-model-applications/
- ISC2, “2024 Cybersecurity Workforce Study.” https://www.isc2.org/Insights/2024/04/ISC2-Cybersecurity-Workforce-Study
- NIST, “Artificial Intelligence Safety Institute Consortium (AISIC).” https://www.nist.gov/aisi
- AI Vulnerability Database (AVID). https://avidml.org/
- MLCommons, “AI Safety.” https://mlcommons.org/
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