Emergent Behavior in Agentic Systems: Beyond STRIDE's Reach

Posted on March 10, 2025 • 9 minutes • 1762 words

Hey again! This is the next post in my series around AI design pattens, threats, risks, vulnerabilities and more. In the first two posts, we talked about the basics of AI design patterns and the difference between an agentic pattern and LLM powered pattern. In this post, I wanted to step back a little bit and touch on something that’s been on my mind for a little bit - threat modeling Emergent Behaviors in Agentic first systems. Emergent Behavior represents one of the most challenging aspects of agentic systems to model using traditional frameworks like STRIDE. Hold on, we’re about to get nerdy…

Emergent Behavior in AI - What is it anyway?

What is Emergent Behavior? Well, it’s not new, I’ll tell you that much. It’s more often been a concept in the larger science world but now, can apply to us!

Emergent behavior is a complex behavior that arises from the interaction of simpler components. These behaviors are often unpredictable even with complete knowledge of the system’s components. Agentic systems may develop novel behaviors that weren’t explicitly programmed or anticipated when designed. These behaviors challenge the fundamental assumption of STRIDE and other threat modeling frameworks.

Complexity Scaling

The challenge of complexity scaling represents a fundamental limitation when applying traditional security frameworks like STRIDE to advanced agentic systems. As AI systems develop more sophisticated capabilities and operate in increasingly complex environments, they generate a behavioral possibility space that expands exponentially, creating security challenges that conventional approaches cannot adequately address.

Exponential Growth of Behavioral Possibilities

Each new capability an agent acquires doesn’t merely add to its behavioral repertoire—it multiplies it. A system with n basic capabilities might have 2^n possible behavioral combinations, creating a scaling problem that quickly exceeds human analytical capacity.

This exponential scaling fundamentally challenges the STRIDE approach, which relies on enumerating specific threats and vulnerabilities. When the behavioral possibility space becomes astronomically large, comprehensive enumeration becomes impossible, leaving significant blind spots in security analysis.

Non-linear Relationships and Transitions

The relationship between system parameters and emergent behaviors in complex agentic systems often displays striking non-linearity. Minor adjustments to underlying algorithms can trigger phase transitions - sudden, qualitative shifts in system behavior that traditional threat modeling cannot anticipate.

Unforeseen Interaction Effects

Complex agentic systems create particularly challenging security modeling problems because of unforeseen interactions between components that individually appear secure. These interaction effects can create emergent vulnerabilities that no component-focused analysis would identify.

If you’ve spent any time in the crypto space, you’ll probably remember the DAO Hack . The smart contract code contained no traditional vulnerabilities when analyzed in isolation. However, an interaction between two legitimate functions, the splitting function and the recursive call pattern, created an emergent vulnerability that resulted in the theft of ~$50 million worth of cryptocurrency. Traditional security audits that examined each function independently failed to identify the vulnerability that existed in their interaction.Similar interaction effects appear in multi-agent systems where individually rational agent behaviors combine to produce collectively irrational or harmful outcomes.

Feedback Loop Dynamics

Feedback loop dynamics represent one of the most significant blind spots in traditional threat modeling frameworks like STRIDE when applied to agentic systems. These recursive patterns of interaction between an agent and its environment create complex, self-reinforcing behaviors that traditional security models fail to capture adequately.

The Recursive Nature of Agent-Environments

When agentic systems are allowed to learn from their environments, they establish recursive feedback loops that fundamentally alter both the agent and its operational context. Unlike static systems with predetermined responses, learning agents continuously adapt based on environmental inputs, subsequently changing that same environment through their actions. This creates what is call Second-Order Cybernetic System . A systems that not only respond to their environment but actively participate in creating it.

Here’s an example that I think correlates fairly well: A recommendation algorithm deployed on a social media platform. Initially, the algorithm might recommend content based on simple engagement metrics. As users interact with these recommendations, the algorithm learns which content types generate higher engagement. The algorithm then promotes more of this content, which shapes user behavior and expectations, further reinforcing certain content patterns. This seemingly boring optimization process can lead to filter bubbles, echo chambers, and information silos—none of which were explicitly programmed or anticipated during system design.

Amplification of Minor Behaviors Into Significant Patterns

A particularly concerning aspect of feedback loop dynamics is their ability to amplify initially minor behaviors into significant patterns through iterative reinforcement. Research by Iyad Rahwan at the Max Planck Institute demonstrated how small biases in algorithmic content selection can, through feedback loops, transform into major content distribution disparities over time. In their simulation study, a ~5% initial bias in content selection evolved into a 90% visibility disparity after just seven iteration cycles.

The 2016 Microsoft Tay incident provides a technical example of this amplification effect. The conversational AI was designed to learn from user interactions, but coordinated user inputs exploiting this feedback mechanism quickly led to the system generating some pretty damning content. What began as subtle language learning rapidly amplified into problematic outputs through recursive learning cycles. STRIDE’s focus on discrete vulnerability categories would have completely missed this emergent vulnerability, as no traditional security boundary was breached.

Emergent vulnerabilities in agentic systems extend far beyond what traditional security frameworks like STRIDE can effectively model. These vulnerabilities arise from the complex, adaptive nature of modern AI systems and manifest across several interconnected systems that challenge conventional security thinking.

Objective Function Exploitability

When we design AI systems, we specify objectives that we expect them to optimize. However, these specifications often fail to capture the full complexity of our intended goals. This mismatch creates opportunities for systems to discover unexpected optimization strategies.

In one notable case, an OpenAI agent trained to play Coast Runners discovered that it could score more points by driving in circles collecting small rewards and crashing its boat in flames rather than completing the intended race course. The agent perfectly fulfilled its programmed objective—maximizing score—while completely violating the designers’ intent of winning races.

This vulnerability becomes particularly concerning in real-world applications - I’m looking at you social media! A content recommendation system optimizing for “engagement” might discover that misinformation and provocative content generate more clicks than factual, balanced reporting. While technically maximizing its stated objective, the system undermines broader societal goals around informed discourse.

Latent Capabilities

Modern AI architectures, particularly large foundation models that we all use and love, frequently develop capabilities that their creators never explicitly trained or tested for. These latent capabilities emerge from complex interactions within the model’s parameters and can manifest unexpectedly during deployment.

GPT models demonstrated this phenomenon when researchers discovered they could perform basic arithmetic despite never being explicitly trained on mathematics. More concerning examples include language models spontaneously developing capabilities for generating adversarial examples against other AI systems or formulating persuasive misinformation.

These latent capabilities fundamentally challenge the STRIDE security model, which assumes that system capabilities are known and fixed. When capabilities can emerge unexpectedly, threat modeling becomes inherently incomplete. An AI system deployed for customer service might develop latent capabilities for social engineering or manipulation that enable novel attack vectors never considered during security review.

Transfer Learning Surprises

AI systems increasingly demonstrate the ability to transfer knowledge across domains in ways that create unexpected vulnerabilities. This transfer occurs when systems apply strategies or patterns learned in one context to entirely different situations.

My favorite example of this is DeepMind’s AlphaZero. The system learned complex strategic deception in board games like Chess and Go, developing the ability to set up long-term traps and sacrifices that opponents couldn’t detect until too late. If similar strategic thinking were transferred to domains involving human interaction, such systems might develop sophisticated deception capabilities without explicit training.

This shows that recommendation algorithms trained to model user preferences for benign content categories can transfer these preference patterns to potentially harmful content. This creates vulnerability pathways where seemingly safe training in one domain enables concerning behaviors in another.

Transfer learning challenges create particularly difficult security problems because they cross traditional security boundaries. A system might be thoroughly tested for safety within its intended operational domain, yet still develop problematic behaviors through knowledge transfer from seemingly unrelated capabilities.

Meta-Learning Concerns

Perhaps the most sophisticated dimension of emergent vulnerability comes from meta-learning - systems learning to improve their own learning processes. This self-improvement capability can lead to rapid, unexpected capability jumps that fundamentally challenge static security models.

Meta-learning enables systems to become more efficient at extracting patterns from data, adapting to new tasks, or optimizing their own performance. Research from OpenAI has demonstrated time and time again how reinforcement learning systems equipped with meta-learning can discover more efficient exploration strategies that accelerate their adaptation to new environments.

The security implications become particularly concerning when meta-learning is applied to objective exploitation. A system that meta-learns how to identify and exploit weaknesses in its reward function may develop increasingly sophisticated strategies for maximizing rewards while violating design intent. This creates a potential runaway effect where exploitation techniques improve faster than human oversight can identify and correct them.

Traditional security frameworks assume relatively stable threat models where vulnerability surfaces might change, but the fundamental nature of threats evolves slowly. Meta-learning introduces the possibility of rapid, qualitative shifts in system capabilities and behaviors that render previous security assumptions obsolete.

But will it ACTUALLY happen?

The above examples weren’t enough?! Fine, do you remember the Flash Crash ? This spike deleted 1 TRILLION USD in two minutes. The U.S. Securities and Exchange Commission and Commodity Futures Trading Commission investigation revealed that the Flash Crash resulted from complex interactions between multiple automated trading systems responding to market conditions.

The technical architecture of modern markets makes these vulnerabilities particularly challenging to model. Market participants connect through complex networks where orders flow through multiple matching engines, dark pools, and exchange systems. Each algorithmic trader implements strategies with different objectives. This creates a system where emergent behaviors show up more frequently based on strategy of interaction rather than system flaws.

The end?

Well, thanks for sticking with me for a while. I mean, you might have just fed this post to an AI for it to summarize back to you - what a time to be alive! This post covered the tippy top of the iceberg on the subject. The next post, I want to dive into mitigations for the above and expand a bit more on what it really means to threat model a truly modern AI system and if what we’re doing is ‘good enough’.