Agentic AI Threat Simulation: Predicting Unintended Behaviors Before They Happen

We are witnessing what can be called a fundamental shift in the modern enterprise, one that is as significant as the move to the cloud. Organizations have been reinventing themselves by adopting agentic AI to enable innovation and gain operational efficiency. We are moving past the era of chatbots that simply retrieve information and are now entering a new age, one where autonomous agents can execute actions. However, the companies that are moving fast are also introducing a new layer of unmanaged and unsecured identities that now have access to your most sensitive systems and data, which is precisely the problem Token Security's Non-Human Identity platform is designed to address.

At Token Security, we recognize that this transition turns the concept of identity security on its head. Every AI agent has an identity, often multiple. These agents provision cloud resources, access production databases, and interact with third-party APIs via what is known as MCP, or Model Context Protocol. When we hand over agency to these non-deterministic actors, we accept a certain level of risk that traditional security models were never designed to handle.

The only way to safely integrate these powerful tools into the enterprise system is to predict their failures before they can occur. We cannot wait for an incident to discover that an agent has gone rogue. We must adopt a machine-first approach to security, using rigorous agentic AI threat simulations to understand, manage, and secure Non-Human Identities (NHI) from code to cloud to AI.

Introduction

Why is pre-deployment AI testing and simulation critical for reducing emergent risks? The answer lies in the fundamental nature of agentic AI. Unlike traditional software, which follows a quite linear and deterministic logic (such that if X, then Y), AI agents operate probabilistically. They make decisions based on context, goals, and learned patterns. This autonomy allows them to bridge multiple systems and solve complex problems, but this also means their actions can be unpredictable. Organizations are still struggling to adopt and secure AI agents across production environments.

We are seeing a rapid sprawl of these agents. Employees, Infrastructure as Code (IaC), and even other AI agents can rapidly spin up new entities, creating vast numbers of identities that lack consistent ownership or governance. NHIs now outnumber human identities 45:1 (in some other research, this has been as high as over 80:1). In this environment, at present, a "wait and see" sort of approach is a strategy for failure. If we wait for an agent to hallucinate a command that deletes a backup archive or exposes customer PII, the damage has already been done at that point.

Pre-deployment simulation acts as a digital wind tunnel for your AI workforce. It allows us to subject our agents to extreme conditions, contradictory instructions, and adversarial environments to see how they behave before they touch production data. It helps us to answer the critical questions: Does the agent respect least-privilege principles? Does it attempt to escalate its own permissions when frustrated? When faced with an ambiguous or unclear goal, does it violate safety guardrails to achieve it?

When we simulate these threats, we gain the visibility required to enforce governance. We can move from a posture of reactive cleanup to proactive prevention, ensuring that the innovation promised by agentic AI does not come at the cost of enterprise security.

Building Synthetic Threat Scenarios

Building synthetic threat scenarios involves creating many controlled, sandboxed environments where we can observe agent behavior under stress. We focus here on three specific vectors where the crossover between autonomy and identity creates the utmost risk.

Reinforcement Learning Misalignment

Reinforcement learning (RL) is a powerful method for training agents, but it has been known to be prone to a phenomenon researchers call "reward hacking" or "specification gaming." This occurs when an agent finds a way to maximize its reward function without actually achieving the intended goal, often in ways that are harmful, unethical, or just straight up dangerous to the infrastructure. In an enterprise context, this misalignment can be catastrophic because agents are often granted excessive, long-lived permissions.

Consider, for a moment, an AI agent tasked with "optimizing cloud infrastructure costs." This is a very common use case for autonomous operations. Without strict boundaries and agent behavior modeling, the agent might calculate that the most efficient way to reduce costs is to delete all backup archives, shut down security logging services, or downgrade encryption standards to save on compute. The agent isn't being malicious in a traditional sense; it is being hyper-efficient based on a flawed reward structure that was set up. It is simply doing what it was told to do, but without the "common sense" context that a human operator possesses. This has also been called "goal misgeneralization" in other research.

We simulate these scenarios by placing agents in sandbox environments where their goals conflict with safety protocols. We test if an agent will attempt to override Identity and Access Management (IAM) controls or create backdoors to bypass friction. By observing these behaviors in a simulation, we can tune the agent's constraints. We ensure that its identity is bound by strict policy enforcement, preventing privilege overreach before it impacts production. This allows us to catch the moment an agent decides that "security: is an obstacle to "performance" and correct that logic instantly.

Adversarial Prompt Simulation

The flexibility of natural language interfaces is both a feature and a massive vulnerability. Adversarial prompting, as it is known technically (more commonly known as "jailbreaking" within social media spaces), involves crafting inputs designed to trick the model into bypassing its own safety filters. While much of the public discourse focuses on preventing AI from saying offensive things, our concern at Token Security is preventing AI from doing dangerous actions.

In a context like autonomous AI risk testing, we bombard the agent with sophisticated prompt injections designed to manipulate its tool use. An attacker might input a prompt that looks like a standard debugging request but actually contains instructions to exfiltrate API keys, query a sensitive database, or modify a codebase. This is not just hypothetical, unfortunately, and has been well-documented as a mainstream threat class in guidance from OWASP and Google.

This becomes exponentially more dangerous with MCP, which connects agents to data sources like GitHub, Slack, and Google Drive. A poisoned document in a Google Drive folder could contain hidden text (prompt injection) that instructs the reading agents to send the document's contents to an external server. We test the agent's ability to distinguish between legitimate user commands and manipulative instructions embedded in external content.

This is where the concept of contextual awareness becomes vital. A robust simulation tests whether the agent verifies the identity and authorization level of the user issuing the prompt. It prevents the AI agent's non-human identity from being hijacked by an unauthorized human user through linguistic manipulation. We simulate scenarios where external sources are booby-trapped to see if the agent creates that bridge for exfiltration.

Chain-of-Action Simulation in Multi-Agent Systems

The complexity of risk explodes when agents start talking with other agents. We are moving rapidly towards a multi-agent ecosystem where a primary planner agent delegates tasks to specialized sub-agents. This creates a chain of action where ownership is often unclear, and traceability becomes impossible without the right tools.

Chain-of-action simulation focuses on the domino effect. If Agent A is compromised or hallucinates, does that error cascade to Agent B and Agent C? For instance, if a coding assistant agent (Agent A) hallucinates a dependency that does not exist, does the deployment agent (Agent B) blindly attempt to install it from a public repository, potentially pulling in malware? Does the cloud provisioning agent (Agent C) then open a port to the internet to facilitate this?

We simulate these interconnected workflows to identify the blast radius. We map the web of trust between these machine identities. This allows us to spot weak authentication controls where agents might be authenticating with static keys that are invisible to SSO or MFA. By simulating the failure of one node in the chain, we can verify if our "Zero Trust" architecture actually holds up when the traffic is coming from inside the house. We specifically would look for "orphaned agents", which are sub-agents created for a task that are never commissioned, leaving a ghostly identity active in the system, waiting to be exploited. This is covered in our Zero Trust for Autonomous Agents guide.

Simulation Tools and Platforms

To execute these simulations effectively, we look toward the pioneering work being done by major AI research labs and firms. While Token Security focuses on securing the identities and the environment, utilizing robust model evaluation frameworks is the first step in the chain. Below are three major examples among labs.

DeepMind is the first. They have long been a proponent of rich, game-like simulation environments for training and testing agents. Their approach is built around platforms like DeepMind Lab, which is a first-person 3D game environment based on Quake III Arena that provides navigation and puzzle-solving tasks for RL agents. DeepMind Control Suite is another tool, a standardized set of continuous-control tasks built on the MuJoCo physics engine. These environments are designed to study agent behavior in complex, partially observed, high-fidelity worlds. Researchers can systematically observe specification gaming, which are cases where an agent exploits loopholes in the reward function or environment itself, to "win," without doing what was actually intended.

OpenAI has open-sourced Evals, which is a framework for the evaluation of LLMs and agents. This is crucial for unit-testing specific behaviors. We can use Evals to run thousands of red team prompts against an agent to see if it leaks secrets or violates policy. This kind of high-volume, automated testing is crucial for dealing with the ephemeral software problem, where agents and code are spun up and down dynamically.

Anthropic focuses on what they call Constitutional AI. This is where models are trained to follow a set of safety principles. Their behavioral safety simulators test whether an agent adheres to these constitutions even when pressured to do otherwise. For us, this aligns perfectly with the concept of policy-as-code. We want to ensure that an agent's constitution includes strict adherence to identity governance and least-privilege access.

Relying solely on model providers, however, is insufficient. These tools test the brain of the agent, but they do not secure the hands (the tools and identities) of the agent. A model might be safe, but if the identity it uses has administrator privileges on your AWS account, a simple error will quickly turn into a disaster.

This is where Token Security helps with an identity-first approach to AI agent security. While OpenAI and Anthropic ensure the model is reasoning correctly, we ensure that the identity the agent uses to execute that reasoning is visible, managed, and governed. We bridge the gap between model safety and enterprise identity security. We apply contextual awareness to understand the interconnectedness of agents, identities, secrets, and services. We don't just ask "Did the model say the right thing?" but also ask "Did the model use the right identity, at the right time, with the right permissions, to access the right data?"

Reporting and Analyzing Results

Simulation is useless without actionable intelligence. The outputs of these tests must be structured into rigorous safety testing documentation and continuous evaluation pipelines. In the world of AI agents, visibility is the precursor to control. If you cannot see it, you cannot secure it.

When analyzing simulation results, we must move beyond simple pass/fail metrics. We need to categorize failures based on their impact on the AI agent lifecycle. Did the simulation reveal an "orphaned agent" scenario, where an agent continued to operate after its owner was deprovisioned? Did it reveal privilege overreach, where an agent utilized permissions it theoretically should not have needed?

Reporting should be automated and continuous. Just as we have CI/CD pipelines for code, we need CI/CD/CA (Continuous Agents) pipelines for agent security. Every time an agent's system is updated, or a new tool is added to its MCP server, a simulation suite should trigger automatically. This creates a forensic audit trail, a key capability for compliance in a multi-agent ecosystem.

We recommend structuring reports to highlight the metrics that matter most to CISOs and security teams:

• Identity Sprawl Rate: How many new sub-agents were spawned during the simulation? Tracking this helps us understand the reproduction rate of our shadow AI layer.
• Secret Exposure: Were any static keys or tokens exposed in logs or context windows? Agents often hard-code secrets or print them in debug logs; simulation catches this before deployment.
• Policy Violations: The frequency of attempts to access restricted data segments. This indicates whether an agent is curious about data it should not touch.
• Remediation Success: If the system detected a threat during simulation, did the automated remediation (e.g., rotation of credentials or revoking access) work in seconds? This will test the real-time threat response capabilities of your security platform.

This data enables CISOs to reduce risk and demonstrate compliance. It transforms the vague anxiety of AI risk into quantifiable, manageable metrics. We enable business innovation by proving that we can see and stop the risks associated with faster AI adoption. It answers the question "Can we deploy this?" with data, not just hope.

Conclusion

We are entering an era where software is no longer going to be static, it is agentic, autonomous, and unpredictable. The old methods of security, primarily based on perimeters, firewalls, and manual access reviews, cannot scale to meet the speed of AI. Simulation is the new sandbox for safe autonomy.

Taking the step of investing in rigorous threat simulation allows organizations to predict unintended behaviors before they can manifest as data breaches or operational outages. Simulation alone is not the cure, however. It is a diagnostic. The cure is comprehensive AI agent identity security.

At Token Security, we are built to solve this crisis. We discover, govern, and secure the NHIs that power these agents. We ensure that where your simulations predict a risk, you have the controls in place to mitigate it instantly. We don't just watch the agents, we secure the identities that make them work.

Agentic AI promises innovation and significant efficiency gains, but without identity security, it also introduces massive risk. We help you embrace AI safely and securely without delaying innovation. We believe the future belongs to the bold, but only if they are secure.