Agentic AI Security Risks: Why Most Saa S Companies Get Open Claw Wrong

Picture this: One viral open-source AI agent, Open Claw, is quietly adopted across Saa S teams hungry for workflow automation. Days later, security researchers uncover over 1,800 unprotected Open Claw servers leaking API keys, chat histories, and master credentials. Suddenly, the same tool that promised supercharged productivity is now a hacker’s playground. This isn’t a hypothetical, this is happening right now, and most Saa S companies are far less prepared than they think. In the world of agentic AI security risks, the biggest threat isn’t just a new vulnerability. It’s our old habits and assumptions that leave us blind.

What Are Agentic AI Security Risks?

Agentic AI security risks refer to the threats introduced by AI agents that can make decisions, take autonomous actions, and access critical workflows, often without traditional oversight or controls. Unlike classic chatbots or content generators, agentic AIs connect directly to tools like Slack, email, and databases, automating real business processes. This autonomy exposes new attack surfaces. According to OWASP’s Top 10 for Agentic Applications (2026), these risks break down into key categories:

• Bad Actions & Tool Misuse: Agents can execute harmful operations if tricked by malicious inputs, resulting in unintended consequences.
• Identity & Privilege Escalation: Agents often possess broad credentials. Once compromised, attackers move laterally within infrastructure.
• Data Breaches & Exposure: Agents processing business data can leak sensitive information far more rapidly and widely than classic software.
• Supply Chain Weaknesses: Third-party APIs, libraries, and automations become new entry points for attacks.
• Lack of Transparency & Accountability: Agents make decisions in the dark, making oversight and auditing hard for security teams.
• Compliance & Governance Gaps: AI-driven activity is outpacing existing regulatory frameworks, increasing compliance headaches.

As one CISO recently put it, "Agentic AI shifts the threat from what AI says to what AI can do." And what it can do is often invisible until it’s too late.

Why Most Companies Get Agentic AI Security Wrong

Most teams assume open-source AI agents like Open Claw work within existing security perimeters. But in practice, traditional firewalls and identity controls weren’t built for autonomous actors that bypass login forms, accumulate privileges, and work between systems 24/7. Saa S security leads and Dev Ops make several recurring mistakes:

• Assuming Accountability: Treating agents as “just another API client” rather than a new class of identity and actor in the system.
• Blind Trust in Defaults: Deploying open-source agents without hardening or continuous monitoring, Open Claw, for example, shipped with UI panels and control end-points exposed by default.
• Failure to Inventory: Lacking real-time discovery and inventory management of every agent and their privileges, especially as non-security staff spin up automations.
• Missing Ownership: No clear ownership for ongoing governance, patching, and lifecycle management of deployed agents.
• Inadequate Logging and Visibility: Treating agents as “black boxes.” When a breach occurs, there are few audit tails or real-time alerts.

Consider the analogy of a wildlife biologist introducing a new animal into a carefully controlled ecosystem. Everything seems fine, until the animal quickly adapts, moves beyond expected boundaries, and disrupts the whole system in unexpected ways. That’s what’s happening with agentic AI in Saa S, except the “new animal” can move data, issue commands, and never needs to sleep or ask permission.

Real-World Open Claw Vulnerabilities: What the Headlines Say

Open Claw is a cautionary tale about open source AI agents. Its meteoric rise brought with it high-severity risks:

• One-click Remote Code Execution (RCE): Attackers could run hostile code just by getting an admin to open a malicious webpage, enabled by insecure Web Socket handling (CVE-2026-25253).
• Prompt Injection and Data Exfiltration: Unsanitized web and email inputs fed into the agent trigger hidden attacks and leakages, often invisible in logs.
• Token & Credential Leaks: Open Claw exposed authentication tokens and API keys via unprotected URLs and misconfigured panels.
• Plaintext Credential Storage: Agents ran with shell/network privileges, sometimes storing secrets in discoverable local files vulnerable to infostealers.
• Widespread Misconfigurations: Over 1,800 Open Claw instances were found fully public, with exposed controls and databases.

Security firms call this a “Whac-A-Mole” scenario. Each exploit gets a frantic wave of patches, but core practices (like granting shell access or not isolating data) remain common. These failures are not unique to one open source project but highlight a wider issue in workflow automation security for Saa S.

How Do AI Agents Expose Saa S Companies to New Threats?

Here’s the blunt truth: AI agents operate with more autonomy and privilege than most Saa S users. Unchecked, they create blind spots and amplify old mistakes:

• Excessive Permissions: Once an agent is granted broad API or database access, attackers can step in and exploit those permissions.
• Cross-Saa S Data Flows: Agents connect services that were never designed to share data by default, enabling silent exfiltration.
• Continuous 24/7 Operation: Unlike humans, agents run automated tasks around the clock, often outside business hour monitoring.
• Shadow IT Risks: Non-security staff sometimes deploy agents without formal review or ongoing oversight, expanding attack surfaces invisibly.
• Prompt Injection Attacks: Malicious prompts or user input can hijack agent workflows (for example, by turning a helpdesk summarizer into a data thief).

Security researchers stress that this isn’t just an “AI governance” problem, it’s a Saa S security, identity, and data exposure problem that spans every platform your agents touch.

Traditional Saa S Security vs. Agentic AI Security: A Hard Comparison

Traditional Saa S Security	Agentic AI Security Needs
Human identities, login tracking, periodic access review	Continuous agent-run automations, inventory of agent permissions/identities
Data boundaries enforced by UI/role controls	API and cross-service boundaries, prompt filtering, real-time data flow analysis
Periodic security audits and logging	Continuous behavior monitoring, human-in-loop for high-value tasks
Least-privilege access and MFA for users	Zero-trust for agents, centralized AI gateways, audit trails for every agent action

The Better Way: How Saa S Teams Can Avoid Blind Spots

So, what’s the answer? It’s not “no more agentic AI,” but it is time for a new playbook. Here are the field-tested recommendations from security researchers, CISOs, and engineers:

• Treat agents as first-class identities: Inventory every agent, log every privilege and data touchpoint, and set automated alerting.
• Enforce the principle of least privilege: Never grant more access than necessary, review and restrict regularly.
• Strengthen monitoring: Real-time detection of abnormal agent behavior and prompt injection attacks is critical.
• Human-in-the-loop checkpoints: For high-value or unusual actions, require human review before the agent executes.
• Secure the supply chain: Audit every 3rd-party module or "skill" that agents use before deployment and on update.
• Pair workflow automation with security automation (like Gleap does): Gleap works near this “exposure zone,” so it’s extra important to secure feedback, support, and automation flows with least-privilege agent tokens and tight data controls.

As the old baseball saying goes, “You can’t defend what you can’t see.” The new priority is visibility, oversight, and building agentic AI security, before someone else builds the breach.

Takeaway: Security Is a Moving Target, But Companies Can Catch Up

Agentic AI security isn’t just tomorrow’s risk, it’s today’s priority, especially for products, support, and feedback teams eager to automate. Open Claw’s story is a wake-up call. The real “next blind spot” is failing to invent new security muscle as agentic AI moves deeper into Saa S ecosystems. Those who start treating every AI agent and automation flow as a potential security risk, audited, governed, monitored, have the best shot at safe, scalable digital transformation. As the generative era unfolds, proactive Saa S teams will be the ones saying: Yes, we use agents. But we see, secure, and control them every step of the way.