Topic starter
25/10/2026 11:10 am
TL;DR: Coding agents can already reach code, secrets and delivery workflows, and Manifold Security says that makes guardrails too weak to be a reliable boundary. Runtime authorisation and tighter scope limits need to define what an agent can do before multi-step actions spill past the task.
Editorial analysis by NHI Mgmt Group, based on content published by Manifold Security: “10,000 Vulnerabilities in 30 Days. Picture This Threat Inside Your Stack.”.
By the numbers:
- The model's true-positive rate on triaged findings sits at 90.6%.
- Anthropic said the program expanded from roughly 50 partners to around 200.
- The update says the program expanded to around 150 additional organizations.
Key questions
Q: Why do guardrails fail to secure agentic AI workflows?
A: Guardrails fail because they are probabilistic and operate on model output, while the risk lives in the execution chain.
A: Treat the agent’s environment as the security boundary.
Q: What breaks when coding agents have broad access to code, secrets and pipelines?
A: The control that breaks is the assumption that software can be safely trusted with broad standing privilege just because it is running inside a development workflow.
Practitioner guidance
- Restrict agent authority to task-scoped access Remove broad standing permissions from coding agents and grant only the minimum resources needed for the current task, with explicit separation between read, write and deploy actions.
- Instrument agent activity at the tool layer Log every repository read, shell command, API call and secret access so you can reconstruct the full execution chain rather than relying on prompt logs alone.
- Define behavioural baselines for each agent Establish what normal access patterns look like for each coding agent and alert when it reaches outside its expected files, services or time windows.
Bottom line: Glasswing shows that coding agents can already create meaningful security risk because their authorised access extends into code, secrets and delivery workflows.
Glossary terms: Guardrails, Coding Agent, Runtime Authorisation, Identity Blast Radius
Explore further
View Full Forum → | NHI Foundation Course → | Our Services → | Read the full analysis →
This topic was modified 7 days ago by NHI Mgmt Group
27/10/2026 7:17 pm
Guardrails are not a trust boundary for coding agents: Anthropic's Glasswing update reinforces that behaviour filters cannot replace identity and authorisation controls. The issue is not whether a model sometimes refuses a dangerous request, but whether the surrounding system lets that agent reach code, secrets, pipelines and deployment paths in the first place. The practical conclusion is that agent governance has to start with access scope, not model restraint.
A few things that frame the scale:
- 19% of organisations give AI systems dramatically more access than human employees, nearly one in five granting unrestricted privilege, according to the 2026 Infrastructure Identity Survey.
- Systems with least-privileged AI access had a 17% incident rate vs 76% for over-privileged systems. Organisations failing to scope AI access properly are 4.5x more likely to experience a security incident, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: How can security teams tell whether AI agent access is drifting out of scope?
A: Look for agents touching systems, data sets, or tools that are outside the intended task boundary, especially when those actions are not part of the approved workflow. Behavioural baselines, entitlement logs, and cross-system correlation are the key signals. If the agent can act meaningfully outside its original purpose, scope drift is already happening.
👉 Read our full editorial: Anthropic's Glasswing shows guardrails fail for coding agents
This post was modified 7 days ago by NHI Mgmt Group