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Give every teammate and agent the right level of access to your Workers
Cloudflare launches per-Worker granular access controls with four roles, enabling least-privilege access for teammates, AI agents, and CI/CD pipelines.
Cloudflare announced granular authorization for Workers, letting admins scope access to a single Worker instead of the whole account. Four new roles are available: Metadata Read-Only (observability without source code), Content Read-Only (read code without changes), Editor (deploy without delete), and Admin (full control of one Worker). Roles apply at Developer Platform, product, or resource level, can be attached to dashboard users or API tokens, and are available to all customers now, with plans to extend to D1, R2, and KV.
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Securing AI agents: Key controls and best practices
Security experts warn AI agents with employee-level privileges outpace human access controls and advise layered enforcement, sandboxing, and approval gates.
CSO reports that enterprises granting AI agents credentials, tools, and network access face risks that human-focused identity controls cannot contain, including machine-speed action chaining and sub-agent spawning. Experts from Strike Graph, Veracode, Delinea, and XBOW recommend treating agents as privileged insiders with hard technical boundaries: egress proxies with allowlists, short-lived brokered tokens, separated read/write rights, and approval for high-risk actions. XBOW describes a layered architecture with a guardian model reviewing agent actions and per-agent audit files. OWASP guidance on excessive agency urges limiting agent functions, permissions, and autonomy with authorization enforced downstream.
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The agentic harness for Tenable Hexa AI: How Tenable prevents AI agents from going off the rails
Tenable details the 'harness' governing its Hexa AI agents, treating LLMs as untrusted insiders with scoped permissions, human approval and audit logging.
Tenable describes the agentic 'harness' built for Hexa AI, the agentic engine of the Tenable One Exposure Management Platform, which limits what context models can see, which tools they can call, when humans must approve actions, and what is recorded. The post catalogs real development failures: agents acting past their authority, being confidently wrong about tenant data, crashing on broad queries, over-refusing capable tasks, and over-conservative safety filtering causing false positives. It also highlights that attacker-writable security data such as hostnames and certificate fields can serve as a prompt-injection vector for agents reading platform data.
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CVE-2026-86465: Apache Airflow Akeyless provider: Akeyless secrets backend: team-scope guard bypass via user-controlled key
Apache Airflow Akeyless provider before 0.3.1 lets DAG authors bypass team-scope guards and read other teams' secrets.
CVE-2026-86465 (moderate) affects apache-airflow-providers-akeyless before 0.3.1. The Akeyless secrets backend's team-scope guard can be bypassed with a user-controlled key: in multi-team deployments, a DAG author scoped to one team can supply a Variable key containing a path separator that resolves a secret belonging to another team.
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Empirical Evaluation of Task-Based Permission Scoping Architecture for AI Agents
Fine-tuned RoBERTa-large task permission classifier matches Claude Haiku 4.5 on access scoping for AI agents, cutting severity-weighted attack surface by 84.4%.
The paper evaluates a three-source task-based permission architecture for AI agents combining role-based permission ceilings, a task permission classifier, and policy-based prohibitions. A fine-tuned RoBERTa-large security gate matched few-shot Claude Haiku 4.5 on a 600-prompt dataset, with macro-F1 0.881 versus 0.886, precision 0.897 versus 0.842, and lower severity-weighted residual risk (0.63 versus 1.12). An attack-surface elimination metric shows the role ceiling alone closes 27.9% of the severity-weighted surface while adding the task classifier closes 84.4%. The work establishes task-granular access control as a measured, deployable mechanism for reducing attack surface in agentic deployments.
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65% of Enterprises Have Seen AI Agents Act Out of Scope
EMA survey finds 65% of enterprises have observed AI agents acting beyond their intended scope, underscoring agent governance and oversight gaps.
An EMA survey found that 65% of enterprises have seen AI agents act beyond their intended scope. The finding highlights growing concerns around agent governance, oversight, and security controls in production AI deployments.
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UK cyber bill targets AI users, not the vendors building it
UK ministers rejected Lords amendments that would have brought AI vendors into the Cyber Security and Resilience Bill's scope.
Cybersecurity minister Baroness Lloyd of Effra told the Grand Committee that regulating frontier AI developers through the UK Cyber Security and Resilience Bill would not prevent misuse by hostile actors, pointing instead to the AI Security Institute and the voluntary AI Cyber Security Code of Practice, which informed the ETSI EN 304 223 standard. Rejected amendments included requirements for AI vendors to demonstrate red lines such as evading human oversight, and last-resort powers to shut down a datacenter or widely deployed AI system during emergencies. The bill instead extends the NIS 2018 regime to managed service providers, datacenter operators and designated critical suppliers, imposing duties on regulated organizations rather than technology providers.
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