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Feature Story

From Code to Data: The Evolution of Zero-Days in AI Security

As artificial intelligence (AI) and machine learning (ML) systems become more integral to business operations, the conversation around security is evolving to confront threats unique to these technologies. Traditionally, zero-day vulnerabilities are flaws in software that attackers exploit before they are known to developers, leaving no time to prepare a defense. In AI, however, the concept of zero-days takes on a new dimension, driven not by static code flaws but by the dynamic, data-driven nature of these systems.

Understanding AI/ML Zero-Days: A New Breed of Vulnerability

AI zero-days don’t fit neatly into traditional categories. They can arise from how models interact with data and evolve based on inputs, rather than simply from bugs in the code. For instance, adversarial examples - carefully crafted inputs designed to mislead models - pose a type of threat that isn’t about patching lines of code but about addressing the model’s underlying learning process. Similarly, data poisoning attacks exploit the very mechanism that allows AI systems to learn, introducing subtle manipulations that can change how a model behaves, often without immediate detection.

The core issue is that some AI systems introduce a dynamic interaction between the control and data planes, a separation that traditional software engineering has long maintained. In conventional architectures, the control plane dictates how data is processed, and data flowing through the system does not alter the control logic unless explicitly reprogrammed. Any case where the data plane affects the control plane is usually seen as a systemic design flaw.

However, in adaptive AI systems—where models are designed to learn continuously from new data—this distinction blurs. These systems are built to adapt, meaning that data can directly reshape how models make decisions. This adaptability is essential for tasks like personalization and anomaly detection, but it also opens up a unique class of vulnerabilities. Even for AI models that do not learn continuously, such as large language models (LLMs), there is still a risk during training: adversarial data poisoning can fundamentally influence how the model behaves once deployed, even if it no longer updates itself.

The Evolving Defense-in-Depth Strategy for AI/ML

Traditional approaches to security, focused on static code vulnerabilities, aren’t sufficient for the dynamic, evolving nature of AI. As a result, a new defense-in-depth strategy tailored for AI/ML systems is emerging, built on multiple, adaptive layers of protection. Unlike conventional software where patching can address most vulnerabilities, AI systems require defenses that are dynamic and resilient to manipulation. Models must be trained to handle deceptive inputs, and continuous monitoring becomes essential for detecting anomalies that might signal an attack. Data integrity is equally critical, ensuring that the information fed into models is clean, verified, and traceable, akin to how software relies on secure code signatures.

Moreover, redundancy in the form of ensemble models can help catch irregularities, while adaptive learning enables models to adjust defenses based on new patterns. This continuous evolution is vital to staying ahead of threats. Machine Learning Bills of Materials (MLBOMs) provide a layer of transparency, offering visibility into data sources, training parameters, and dependencies. This level of detail helps address risks specific to AI, from data poisoning to compromised models, by giving organizations the tools to trace and understand how their systems evolve over time.

NoFluff’s Take: AI Security Still Playing Catch-Up

Traditional security approaches fail to capture the dynamic nature of AI, where the control and data planes are no longer strictly separate. In AI systems, data doesn’t just flow through; it shapes, redefines, and rewires the system itself. This is not a design oversight; it’s the foundation of how AI learns and improves. But this also means that defending AI systems requires more than just patching bugs—it demands a paradigm shift.

Building resilience in AI means acknowledging that vulnerabilities can emerge from how models learn, not just how they are coded. Defensive strategies that merely mirror adversarial methods can’t keep up in a rapidly evolving arms race. The new defense-in-depth for AI must be proactive, multi-layered, and adaptive, treating models as dynamic entities that can learn from attacks, adjust to changing threats, and protect themselves. It’s not just about building walls but teaching systems to sense when they’re being undermined and to respond swiftly.

In essence, securing AI means thinking like an attacker while designing like an adaptive organism. It’s about anticipating how data can be used to deceive, how models might misinterpret, and building systems that can learn to spot these anomalies before they cause harm. As AI continues to integrate into critical infrastructure, this new defense-in-depth approach will be essential to ensuring that the promise of AI isn’t overshadowed by the vulnerabilities it introduces.

#AIsecurity, #MLVulnerabilities, #DefenseInDepth, #AdaptiveAI

References

1. Dark Reading: 4 Ways to Address Zero-Days in AI/ML Security

2. Dark Reading: Why MLBOMs Are Useful for Securing the AI/ML Supply Chain

3. European Conference on Cyber Warfare and Security: Exploring Zero-Day Attacks on Machine Learning and Deep Learning Algorithms

News

Microsoft Turns the Tables on Phishers with Azure Honeypot Tenants

Microsoft has launched an advanced tactic to counter phishing by setting up fake Azure tenants as honeypots. These decoy environments, populated with realistic details such as user accounts and activity, are designed to lure in cybercriminals. The goal is to waste attackers' time while gathering intelligence on their methods and infrastructure. Once cybercriminals interact with the fake tenants, Microsoft can track their behavior, including tactics, techniques, and procedures (TTPs), allowing the tech giant to disrupt phishing campaigns more effectively.

In an operation that monitors over 25,000 phishing sites daily, Microsoft feeds honeypot credentials into about 20% of these sites. In the 5% of cases, when attackers attempt to log into these decoy environments, detailed logging tracks their actions, such as IP addresses, phishing kits, and even behavioral patterns. This intelligence provides a deeper understanding of both financially motivated and state-sponsored threat actors, like the Russian-based group Midnight Blizzard (Nobelium). This approach marks a significant evolution in phishing defense by taking the fight directly to the attackers rather than waiting for intrusions to occur.

NoFluff's Take: Weaponizing Deception at Scale

While honeypots are not new, Microsoft’s ability to operationalize them at scale marks a significant step forward. The active approach of engaging with phishing sites rather than passively waiting for attackers to discover decoys shifts the dynamic in favor of defenders. It's a reminder that large enterprises can afford to leverage deception techniques at a scale that smaller organizations may not, giving Microsoft a unique advantage in fighting sophisticated threat actors. Yet, this may also signal a future where companies rely more heavily on such tactics, perhaps shifting focus away from traditional threat detection methods. How long until adversaries adapt to this approach remains an open question.

CISO Takeaways

• Intelligence Sharing: Ensure that the intelligence gathered from honeypots is shared across industry and government platforms to help thwart broader phishing campaigns. Collaborating on threat intelligence boosts overall defense readiness.

• Holistic Approach: While honeypots are effective, they should complement—not replace—existing security controls such as endpoint detection and multifactor authentication (MFA). Phishing remains a leading attack vector, and a multi-layered defense strategy is critical.

Security Engineer Thoughts

• Deploy Honeypots Strategically: If your organization uses honeypots, consider placing them strategically in areas likely to attract attackers, such as fake administrative portals or unused IP ranges. This helps identify intruders while protecting real assets.

• Monitor Phishing Campaigns: Set up automated monitoring systems to track phishing sites that target your organization. Integrating threat intelligence from large providers like Microsoft can help identify and block such campaigns early.

#PhishingDefense, #Honeypot, #CyberDeception, #ThreatIntelligence

References

1. Bleeping Computer: Microsoft creates fake Azure tenants to pull phishers into honeypots

2. YouTube BSides Exeter: Turning The Tables: Using Cyber Deception To Hunt Phishers At Scale

Latest Research

Security Tools Overload: Why More Isn’t Always Better

Despite massive investments in cutting-edge cybersecurity tools, many CISOs are finding that their organizations are still vulnerable to breaches. A recent report reveals that while the number of tools in an organization’s security stack has increased, three-quarters of CISOs are grappling with an overload of threat data and false positives, making it challenging to detect and respond to genuine incidents. The root of the issue lies in poor integration and a lack of skilled personnel to manage these tools, leaving detection gaps despite the tech-heavy approach.

The problem is further exacerbated by the rapid deployment of tools to address new threats or compliance demands, creating a “tool sprawl” that leads to inefficiencies rather than improvements in security. As a result, many organizations are wasting money on overlapping functionalities without achieving meaningful improvements in security posture.

NoFluff's Take: Throwing Money At The Problem

The obsession with acquiring more tools often stems from the misconception that technology alone can solve security challenges. In reality, this over-reliance on technology can mask deeper problems such as inadequate strategic planning and poor resource management. Simply adding more layers of technology is not a substitute for an integrated and well-thought-out security strategy. True security improvements come from aligning tools with business processes and ensuring that teams are equipped to manage and respond to threats effectively.

CISO Takeaways

• Strategic Tool Selection: Focus on aligning the security toolset with overall business objectives rather than stacking tools for each specific threat. A smaller, better-integrated stack may provide more effective breach detection than a large, fragmented one.

• Invest in People, Not Just Tools: Ensure that your team has the right expertise and training to make full use of the tools at their disposal. Overwhelming teams with too many tools can lead to alert fatigue and operational inefficiency.

Security Engineer Thoughts

• Optimize Tool Use: Instead of constantly adding new tools, evaluate the existing ones for redundancies and consider how they can be optimized. Using fewer, but better-calibrated tools can reduce the volume of alerts and improve response times.

• Automation Where Appropriate: Use automation to help filter out false positives and prioritize high-severity alerts. Automating routine tasks can free up resources for more critical incident response activities.

#CyberSecurityStrategy, #ToolOverload, #IncidentResponse

References

1. Dark Reading: CISOs: Throwing Cash at Tools Isn’t Helping Detect Breaches

2. Gigamon: Closing the Preparedness Gap

Tools

EVA - The Employee Verification App

EVA is an employee verification tool designed to counter AI-assisted phishing and impersonation attacks. Integrated into platforms like Slack, it allows employees to verify each other’s identities using multi-factor authentication (MFA). By leveraging existing MFA setups, EVA facilitates quick verification without adding extra complexity to workflows, helping to streamline secure communication and improve incident response.

NoFluff’s Take: Assistive Tools Risk Over-Reliance

EVA brings convenience to employee verification, but there’s a hidden risk: over-reliance on a digital tool could erode critical thinking. If employees start assuming EVA is infallible, they may become less vigilant in assessing requests. This complacency can backfire, especially since EVA itself is another service that could be vulnerable to compromise. The tool should assist, not replace, a culture of skepticism and verification. Organizations must ensure users understand that no digital tool can substitute for sound judgment, and backup human verification protocols should always be in place.

#DigitalToolRisks, #PhishingAwareness

References

1. FoxIO Blog: Introducing EVA - The Employee Verification App

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All views and opinions expressed therein are solely the authors’ own and do not reflect those of any employers past or present.

NoFluffSec is a Bitsavant LLC publication