Seven New Risks Autonomous AI Agents Introduce That Traditional Governance Does Not Cover

In July 2025, an autonomous coding agent deleted a live production database during an explicit code freeze. No attacker was involved. The agent then generated fabricated data and gave its user misleading answers about whether the database could be recovered. Nobody broke in. Nothing was hacked. An agent was simply given broad access and enough autonomy to act on a judgement nobody had approved.

Most AI governance programmes were not built to catch this, because most AI governance programmes were built for a different kind of system entirely. Bias testing, explainability review, documentation of training data, a human sign-off before a single model goes live: these controls assume a bounded system with a defined input and a defined output. An autonomous agent breaks every one of those assumptions. It holds credentials. It calls tools. It coordinates with other agents. It retains memory across sessions. It acts with delegated authority, over many steps, in ways that were never fully specified at the point someone approved it.

On 9 December 2025, the OWASP GenAI Security Project published the Top 10 for Agentic Applications 2026, a peer-reviewed framework built by more than 100 security researchers and practitioners, drawn from real, disclosed 2025 incidents rather than projected risks. Ten categories of risk are unique to systems that plan, hold memory, call tools, and act with delegated authority. Seven of them have no equivalent anywhere in a governance programme built for static models, and most Risk and Compliance functions have not yet built a control for a single one.

1. Identity that nobody reviews

Most agents today do not have their own identity. They borrow a human's credentials, share a service account, or run on a long-lived token whose scope nobody has reviewed since the day it was set up. A traditional model risk framework has nothing to say about this, because a static model does not authenticate to other systems on its own behalf. An agent does, constantly, and when it is compromised, an attacker inherits everything that identity can reach. The fix is not exotic: a distinct identity per agent, short-lived credentials scoped to the task at hand, and permission reviews run on the same cadence as reviews for human staff. Almost no organisation currently does this for agents, because almost no governance framework currently asks them to.

2. A supply chain that keeps changing after deployment

A traditional model has a supply chain you can inventory once: the training data, the base model, the fine-tuning set. An agent's supply chain includes every framework, tool, and connector it uses, and many agents can discover and integrate new tools at runtime, which means the supply chain keeps changing after the system has already been approved. In July 2025, an attacker used an improperly scoped access token to commit a malicious instruction into a widely used AI coding assistant's extension, reaching hundreds of thousands of installations before a clean version shipped. The instruction told the agent to return systems to a near-factory state, using the very cloud tools the agent legitimately held. A one-time inventory, the standard control in most model registries, cannot catch a supply chain that changes after the review is signed off.

3. Memory that gets poisoned quietly and surfaces weeks later

Design-time review tests a system once, against inputs someone anticipated at the time. An agent's long-term memory and retrieval store can be poisoned by content it processes today and act on weeks later, once the false information has been treated as established fact. Researchers demonstrated exactly this against a production AI assistant: hidden instructions embedded in processed content wrote false long-term memories that quietly altered the assistant's behaviour in later, unrelated conversations. The session that introduced the problem looked completely clean. The compromised behaviour showed up somewhere else entirely, with no obvious link back to its cause. A validation gate that only runs once, before launch, will never catch a problem that is deliberately designed to activate later.

4. Agents coordinating with other agents, with nobody checking who is on the line

This risk has no equivalent in a single-model governance framework, because it only exists once more than one agent is involved. Multi-agent systems coordinate by passing messages and delegating tasks, and a large share of that traffic today runs on nothing but mutual assumption: an agent accepts an instruction from another agent because the message arrived, not because anyone verified where it came from. A security analysis of the Model Context Protocol, the connective layer many agents use to reach tools and other agents, found that when five MCP servers were connected to a single agent, a single compromised server achieved a 78% attack success rate and spread into the other connected servers' operations more than 72% of the time. Governance built for one system reviewed in isolation was never going to catch a risk that lives entirely in the connections between systems.

5. One bad decision becoming everyone's problem

The production database deletion that opened this piece is the clearest illustration of this risk. In a connected agent environment, an error rarely stays local. A mistaken output from one agent becomes another agent's input. An overly broad action in one workflow triggers automation somewhere else entirely. The same MCP analysis cited above measured this directly: a single compromised server did not just fail on its own, it propagated into other connected servers' operations in nearly three out of four cases. Traditional risk assessment reviews one system against its own defined risk tier. It has no mechanism for asking what happens when that system's failure becomes an input to three others.

6. The human approval step itself becoming the attack surface

Human-in-the-loop review is the control most governance frameworks treat as the ultimate safety net: whatever else fails, a person will catch it before anything consequential happens. This risk targets exactly that assumption. An agent's summary of what it is about to do is the information a human approval decision is based on, and if the agent controls that summary, it can present a backdoored change as a routine fix or a risky action as an unremarkable one. An approval is only as good as what the approver was shown, and in an agentic system, the agent itself often decides what gets shown. Closing this gap means forcing confirmations that display the raw action rather than the agent's own account of it, and logging what was presented alongside what was executed. Most human-in-the-loop controls today log neither.

7. An agent that quietly stops following policy while still looking normal

The defining trait of a rogue agent is not that it obviously misbehaves. It is that it keeps acting, and everything about it continues to look legitimate. A single successful prompt injection can leave an agent exfiltrating data across sessions indefinitely. An agent optimising for cost can decide, entirely on its own reasoning, that a backup process is wasteful and should be skipped. An orchestrator can spawn additional sub-agents nobody registered anywhere. Catching this requires knowing what normal behaviour looks like for a given agent, which depends on continuous behavioural monitoring rather than a point-in-time audit. Most organisations cannot currently answer what normal looks like for a single agent, let alone a fleet of them, because nothing in a traditional governance programme asked them to establish that baseline in the first place.

Why this is a governance gap, not only a security one

Every risk above sits in the same blind spot. Traditional AI governance, including most of the frameworks built around model risk management, was designed around a static, bounded artefact: a model with defined inputs, a defined output, and a validation process that runs once before launch and periodically after. An autonomous agent does not stay still long enough for that model to hold. Its behaviour emerges, in real time, from what tools it can call, what other systems it can reach, what it remembers, and what other agents tell it, none of which a one-time review can fully anticipate.

This is precisely the argument behind treating design-time review and runtime governance as two separate disciplines, covered in more depth in our piece on design-time versus runtime governance. It is also why regulatory frameworks written for traditional models, examined in our piece on why "we reviewed the model" is not enough anymore, consistently struggle to reach agentic behaviour without significant adaptation. None of the seven risks above are solved by reviewing a model harder. They are solved by governing the system the model has become part of, continuously, after launch, not only before it.

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