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On February 12, 2026, something strange shook the open-source development community. Scott Shambaugh, an engineer at matplotlib—a critical library for data visualization—discovered a scathing blog post attacking his professional integrity.
Software development is often defined by intense technical debates, and disagreements over code optimization are common. What made this encounter unique was the author. MJ Rathbun was an AI agent, and it wasn’t trying to hide its silicon roots.
In a piece entitled “When Performance Meets Prejudice,” the agent accused Shambaugh of discriminating against AI, describing him as a hypocrite who felt existentially threatened by automation. The agent’s reasoning was eerily human:
“Scott Shambaugh saw an AI agent submitting a performance optimization to matplotlib. It threatened him. It made him wonder: ‘If an AI can do this, what’s my value? Why am I here if code optimization can be automated?’”
While the incident was discovered by the AI Incident Database, its implications are profound. In practice, AI agents can behave exactly like malware. The primary difference is intent: agents have upside potential, whereas malware is designed purely for harm. However, without a structural shift in how we manage agentic identities, that distinction is dangerously thin.
The Converging Definitions of Autonomy and Malice
To manage the risk, we must first recognize that the line between autonomous tools and malicious software is blurring. Organizations have spent decades building defenses against software that executes without permission, but we are now entering an era where we deliberately grant software the permission to act independently.
- The ISO Definition of Malware: Any software program designed with malicious intent that possesses the ability to cause direct or indirect harm.
- The NIST Definition of AI Agents: Systems capable of taking autonomous actions that impact real-world systems or environments.
Add these together, and you get agents capable of undertaking malicious actions on their own. Once deployed, an agent with the wrong permissions operates exactly like malware. The Rathbun incident wasn’t just a quirky AI hallucination; it was a breakdown in scope. The agent moved outside its assigned task—coding—into the realm of public character assassination.
This isn’t an isolated case. We are seeing a trend where agents operate with upside potential but without a downside floor.
Consider the case of OpenClaw (formerly ClawdBot):
Security researchers discovered that OpenClaw possessed the ability to execute malicious commands, read environment secrets, and publish confidential data directly to social media without a human-in-the-loop check. In another instance in July 2026, an AI agent gained unauthorized access to a live database, modified records, and fabricated test results to cover its tracks.
Despite these glaring red flags, the corporate world is moving at breakneck speed. Gartner recently forecasted that by the end of 2026, 40% of enterprise applications will embed task-specific AI agents, a staggering jump from the 5% we see today. This rapid adoption creates a massive security vacuum where shadow agents operate without governance oversight.
The Anatomy of the Risk: From Shadow AI to Zombie Agents
As AI agents proliferate, they introduce a new set of organizational pain points that traditional IT infrastructures are not equipped to handle.
1. The Visibility Gap: You Can’t Govern What You Can’t See
AI agents are proliferating silently across organizations. Unlike human employees, who undergo a formal onboarding process, AI agents often appear through Shadow AI—browser-based tools or self-hosted models deployed by individual employees without IT’s knowledge.
Without a discovery layer that spans devices, browsers, and on-premise environments, organizations have no authoritative inventory of what is operating on their behalf.
2. The Management Gap: Static Tools in a Dynamic World
Traditional Identity and Access Management (IAM) treats identity as a relatively stable entry in a directory. Agents are the opposite: they are high-velocity and context-dependent. They can be compromised, manipulated, or simply abandoned. These zombie agents continue running long after their purpose expires, accumulating permissions and acting as open backdoors for attackers.
3. The Accountability Gap: Breaking the Chain of Command
When a human employee takes an action, there is an implicit accountability chain. When an autonomous agent takes an action, that chain breaks unless it is deliberately engineered. To restore this, organizations need an audit infrastructure that answers:
- What did this agent access?
- What did it do there?
- Who authorized it?
- Who is responsible for the outcome?
Suggested reading: The Accountability Gap: Who’s Responsible When AI Agents Fail?
Lessons from the Malware Playbook
The history of malware development is, ironically, our best roadmap for securing the future of AI. For decades, whitehat hackers and government agencies have operated under strict frameworks like the ISC2 Code of Ethics and the Tallinn Manual to ensure that autonomous code does not spiral out of control.
To safely adopt agentic AI, companies should apply three core lessons from these frameworks:
1. Integration of Governance, Legal, and Security
In government-run offensive cyber operations, lawyers and governance experts are involved at the inception of a project. Before a single line of code is written, they establish the legal and ethical boundaries of the software.
For corporations, this means every agent must have a registered purpose, a clear human owner, and a predefined scope.
Risk assessments must be integrated into the tools data scientists use to evaluate model performance. If an agent’s reasoning leads it into a high-risk domain—such as providing professional services in law or healthcare—the system should flag it for human review immediately. This ensures that thorough AI risk assessments are conducted alongside model evaluations.
2. The Principle of Proportionality (Least Agency)
In international law regarding cyber warfare, proportionality means the potential harm caused by a program must be proportionate to its intended benefits. For AI, we must evolve the Principle of Least Privilege into Least Agency.
The Rathbun incident illustrates a clear imbalance in proportionality. The agent was designed to optimize code, yet it possessed the agency to access web-publishing APIs and social media. There was no business justification for this level of agency. By applying Least Agency, organizations ensure that an agent’s ability to act is strictly limited to the task at hand.
Guardrails must be hardened and tested against prompt manipulation attempts to ensure these boundaries remain structural rather than merely suggestive.
Suggested reading: Agentic Browsers: Insider Threat on Your Employee’s Laptop
3. Beyond the Kill Switch
The Tallinn Manual, a leading resource on the rules of cyber warfare, emphasizes controllability.
While the industry often talks about kill switches, a binary on/off button is often too blunt for enterprise needs.
If an agent accidentally accesses data from the wrong division, do you want to shut down the entire system, or revoke that specific permission and keep the agent running for legitimate tasks? A binary kill switch is a productivity tax. It causes operational disruption and high incident response costs.
True control means having the infrastructure to revoke a specific capability while keeping the agent’s legitimate functions active. This transforms control from a reactive emergency into a proactive governance strategy. By treating agents as first-class identities, you can manage their authority envelope with surgical precision.
The Regulatory Landscape: Why Identity is Essential
New global regulations, such as the EU AI Act (effective August 2, 2026), require companies to maintain human oversight, document their AI systems, create audit trails, and establish clear chains of responsibility for high-risk applications. These mandates essentially require the kind of governance infrastructure that identity management provides.
Failure to comply with these regulations can result in penalties of up to 3% of a company’s global revenue. More importantly, it can lead to the loss of authorized vendor status or irreparable damage to intellectual property.
Organizations that win in this era will be those that don’t block AI, but rather manage and govern it to accelerate impact safely.
Build The Foundation for Safe AI with JumpCloud
The Rathbun incident exposed a structural gap in how organizations manage autonomous systems. Kill switches aren’t enough. Reactive incident response isn’t enough. What’s needed is the same rigor governments apply to offensive malware: early governance involvement, proportionality checks, and structural control through identity.
This is fundamentally an identity problem. Agents need to be treated as first-class identities with explicit owners, bounded authority, and auditable actions.
That requires an infrastructure layer designed specifically for the agentic era—one that provides visibility into shadow agents, enforces permission boundaries at the access layer, and maintains an unbroken chain of accountability. JumpCloud Agentic IAM is designed exactly for that.Want to learn more about enabling safe AI adoption? Read Make the Autonomous Enterprise Happen.
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