Posted by Suresh Sathyamurthy
February 10, 2026
OpenClaw is accelerating the adoption of autonomous AI agents, but it’s also exposing a new class of enterprise security risk. As agent frameworks move from experimentation into real operational use, organizations are discovering that traditional IAM, secrets management, and access controls were not designed for autonomous systems that act continuously, hold credentials, and operate outside formal identity boundaries. OpenClaw highlights why AI agents must be treated as non-human identities and why securing them starts with identity, access, and secrets rather than model behavior alone.
The Sudden Rise of Autonomous Agents, and Why CISOs Should Care
Over the past days, OpenClaw has captured massive attention across the developer and AI communities. As an open-source autonomous agent framework, it demonstrates how quickly AI agents can move from experimentation to real operational use, executing tasks, interacting with systems, and acting on behalf of users.
OpenClaw is not just a novelty project, it has garnered hundreds of thousands of GitHub stars within weeks. Unsurprisingly, this popularity extends to bad actors. Security researchers have already identified dozens to hundreds of malicious extensions being published to its ecosystem. These are not theoretical issues; they are active, observable threats in the wild.
What matters now isn’t just how fast OpenClaw is spreading, but what it reveals about how autonomous agents operate once they leave controlled environments. Autonomous agents fundamentally change the identity and security model inside modern environments. OpenClaw is not just another AI project; it is a clear signal that organizations are entering an era where non-human identities, particularly AI agents, operate with broad permissions, persistent access, and real impact on data and systems.
This moment is less about OpenClaw itself; it’s about what it represents and what that means for enterprise security. Autonomous agents introduce new attack surfaces, supply-chain vectors, and execution paths that traditional security controls were not designed to handle.
What OpenClaw Represents From a Security Perspective
OpenClaw enables AI agents to run locally, integrate with messaging platforms, access files, invoke APIs, and automate workflows. From a security standpoint, this introduces three notable characteristics:
- Agents act with identity – even if that identity is implicit, local, or machine-based.
- Agents require secrets – API keys, tokens, credentials, or encryption material to function.
- Agents operate continuously – not as one-off scripts, but as persistent actors.
Critically, OpenClaw agents do not require centralized orchestration or managed runtimes. They can be executed on developer machines, CI runners, or ad-hoc production hosts, extended dynamically through third-party skills, and granted access through local configuration. This pushes identity decisions closer to the edge than most enterprise security models anticipate.
This combination means AI agents behave more like privileged services than traditional applications. They authenticate, make decisions, and access resources autonomously, often outside established IAM, PAM, or secrets workflows.
Security teams are now confronted with a critical question:
How do we control identities that were never explicitly created, but still hold real power?
Core Security Risks Exposed by Autonomous Agents
1. Identity sprawl without visibility
Autonomous agents frequently authenticate using API keys, local credentials, or embedded tokens. These identities are often invisible to central IAM systems, cloud identity dashboards, or access reviews.
Without discovery and observability, security teams cannot confidently answer:
- What agents exist?
- What systems do they access?
- What permissions do they effectively hold?
This creates blind spots that attackers can exploit, or that well-meaning automation can unintentionally abuse.
OpenClaw ups the stakes by making it easy for agents to be created, modified, and run without any formal identity registration step. In practice, agents may accumulate effective privileges over time without ever appearing as named entities in enterprise identity systems.
2. Secrets exposure becomes systemic
Agents require secrets to function. When secrets are stored locally, embedded in configuration files, or passed at runtime without strong controls, they become easy targets.
Prompt injection, log leakage, misconfigured storage, or plugin abuse can all lead to credential exposure. Unlike human users, agents do not “notice” something is wrong, they continue operating, often amplifying the blast radius.
In OpenClaw’s extensible model, third-party skills can request access to tools, APIs, and local resources as part of normal operation. If secrets are available to the agent at runtime, a malicious or compromised skill can misuse them without triggering traditional authentication failures or alerts.
This is not a vulnerability unique to OpenClaw; it is a structural risk in agent-based architectures.
3. No clear trust boundary
Traditional security models assume clear trust boundaries: users authenticate, applications run in defined environments, and permissions are scoped accordingly.
Autonomous agents blur these boundaries. They combine logic, identity, and execution in a single entity, making it harder to enforce least privilege or isolate risk using legacy controls.
OpenClaw highlights how agents collapse multiple trust zones into one execution context: reasoning, decision-making, tool invocation, and credential use all happen within the same loop. Once compromised, there is often no natural choke point where access can be reevaluated or constrained.
Why Identity Security Must Evolve for AI Agents
The OpenClaw moment highlights a broader industry gap: identity security has focused on humans and services, but not autonomous decision-makers.
AI agents need:
- Strong, verifiable identity
- Just-in-time access instead of standing credentials
- Continuous monitoring and auditability
- Clear ownership and lifecycle management
Without these controls, agents become long-lived insiders with little oversight, a scenario security leaders have spent years trying to eliminate in other contexts.
Akeyless’ Perspective: Powering Autonomous AI with Secure Identity and Secrets
At Akeyless, we view AI agents as an extension of machine identity, not an exception to security principles.
From an architectural standpoint, this means:
- Secrets should never be embedded in agent code or configurations
- Access should be temporary, contextual, and revocable
- Encryption keys and credentials should never be fully exposed, even at runtime
- All access must be observable and auditable, regardless of where the agent runs
This is why Akeyless focuses on secretless patterns, distributed cryptography, and identity-centric access for machines and AI agents alike.
Rather than trusting the agent, the platform enforces trust boundaries around it.
From Awareness to Action: What Security Leaders Should Do Now
The rise of OpenClaw is not a reason to panic, but it is a clear signal that security assumptions must be revisited.
Security teams should begin by asking:
- Where are autonomous agents already running?
- What secrets do they depend on?
- How is their access granted, rotated, and revoked?
- What happens if an agent is compromised or behaves unexpectedly?
These questions require discovery, observability, and remediation, not just policy documents.
What This Means for CISOs
- AI agents are now identities – even when they aren’t formally defined as such. Treat them like privileged machines, not experimental tools.
- Secrets exposure is the primary risk vector, not model behavior. Focus first on how agents authenticate and access systems.
- Visibility must come before control. You can’t govern what you can’t discover or observe.
- Static credentials don’t scale to autonomous agents. Prioritize just-in-time access and secretless patterns.
- Assume agents will proliferate faster than policy. Build guardrails that are automated, auditable, and identity-driven by default.
3 Immediate Actions for CISOs
- Identify your AI agents now
Inventory where autonomous agents already exist – in developer environments, automation scripts, copilots, and internal tools. Assume they are already accessing sensitive systems. - Eliminate embedded and long-lived secrets
Audit how agents authenticate today. Prioritize removing hardcoded API keys, tokens, and credentials in favor of just-in-time, identity-based access. - Establish visibility and auditability
Ensure every agent action, access, encryption, and data movement, is observable, logged, and centrally auditable before agents scale further.
Closing Thoughts: OpenClaw Is the Signal, Not the Story
OpenClaw did not create these risks, it exposed them. Autonomous agents are becoming inevitable across enterprises, whether through open-source tools, embedded AI features, or internal automation initiatives.
The organizations that succeed in this next phase will be those that treat AI agents as first-class identities, secured by design, not patched after the fact.
Security for the AI era is not about stopping innovation.
It is about building guardrails that allow innovation to scale safely.
If you are a security leader thinking through the implications of autonomous AI agents, this conversation is just beginning, and it must start with identity and secrets. Contact Akeyless secrets and identity security experts to explore more.
FAQs
What is OpenClaw?
OpenClaw is an open-source autonomous AI agent framework that allows agents to execute tasks, invoke tools, access files, and interact with external systems with minimal human involvement. Its rapid adoption highlights how quickly AI agents are moving from experimentation into real operational use—and why enterprises need to consider the security implications of agent-based automation.
Why should enterprises care about OpenClaw?
Enterprises should care about OpenClaw because it demonstrates how autonomous AI agents can operate with real access to systems, data, and credentials—often outside traditional identity and access management controls. OpenClaw acts as a signal that AI agents are becoming a new class of non-human identity with security implications similar to, but more dynamic than, traditional machine identities.
What security risks do autonomous AI agents introduce?
Autonomous AI agents introduce risks such as identity sprawl, secrets exposure, unclear trust boundaries, and lack of accountability. Because agents act continuously and autonomously, compromised credentials or malicious extensions can amplify impact faster than human-driven systems, making traditional static credential and access models insufficient.
Are AI agent vulnerabilities about the model or about identity?
In most enterprise environments, the primary risk is not the AI model itself but how agents authenticate and access systems. Poorly managed API keys, long-lived credentials, and lack of identity governance create larger attack surfaces than model behavior alone. Securing AI agents starts with identity, access control, and secrets management.
How can organizations secure AI agents like OpenClaw?
Organizations can secure AI agents by treating them as first-class non-human identities. This includes issuing strong, verifiable identities, eliminating embedded or long-lived secrets, enforcing just-in-time access, and ensuring all agent activity is observable and auditable. Secretless and identity-centric security models are better suited to autonomous agents than traditional credential-based approaches.
