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10/07/2026 9:21 pm
TL;DR: Agentic AI breaks a core security assumption: systems can now chain tools, retry paths, and pursue goals at machine speed, making human intent and traditional functional fixedness unreliable, according to ColorTokens. The decisive variable is privilege, because identity, permissions, and reachability must constrain what agents can do when controls are bypassed creatively.
NHIMG editorial — based on content published by ColorTokens: The Mythos Moment: When Hacking Tools Move from Functional Fixedness to Divergent Hacker Thinking
Questions worth separating out
Q: What breaks when agentic AI is governed like traditional software?
A: Traditional software governance assumes fixed workflows, predictable tool use, and bounded execution paths.
Q: Why do zero trust controls matter more for agentic AI than for ordinary automation?
A: Ordinary automation follows predefined rules, but agentic systems can search for alternate routes and chain permitted actions toward a goal.
Q: How do security teams know whether an agent has too much privilege?
A: The clearest signal is whether the agent can reach systems, data, or tools that are not necessary for the task and still complete its objective.
Practitioner guidance
- Map agent runtime pathways before allowing production access Inventory the tools, APIs, data sources, and network segments an agent can reach, then remove any path that is not strictly needed for the task.
- Redefine least privilege around reachable actions Assess privilege by what an agent can discover and combine during execution, not by the nominal role assigned at provisioning time.
- Segment agent access from human trust domains Separate agent credentials, routes, and policy boundaries from human operator paths so one compromise or scope drift does not expose broader administrative reach.
What's in the full article
ColorTokens' full article covers the conceptual and strategic detail this post intentionally leaves for the source:
- The author’s full explanation of the cognitive idea behind functional fixedness and why it matters in cyber defence.
- The Mythos Preview evidence cited in the article, including the exploit-chaining claims and the operational interpretation.
- The concluding argument tying Zero Trust Architecture to machine-speed divergent behaviour in agentic systems.
- The article’s own examples of how identity, permissions, and reachability work together to constrain unintended action paths.
👉 Read ColorTokens’ analysis of agentic AI, functional fixedness, and zero trust →
Agentic AI and zero trust: what changes for identity teams?
Explore further
10/07/2026 9:44 pm
Functional fixedness is no longer a safe assumption for identity governance. Traditional IAM and PAM models assume a bounded actor that follows intended workflows, but agentic systems can search for alternate execution paths at runtime. That breaks the premise that access can be reasoned about only from the original request. The implication is that governance must be built around reachable action, not just declared role.
A few things that frame the scale:
- 64% of valid secrets leaked in 2022 are still valid and exploitable today, proving that detection alone is not enough without automated revocation, according to The State of Secrets Sprawl 2026.
- AI-related credential leaks surged 81.5% year-over-year in 2025, with the surrounding AI infrastructure leaking 5x faster than core LLM providers.
A question worth separating out:
Q: Who is accountable when an agent crosses an intended boundary?
A: Accountability sits with the organisation that defined the permissions, the policy, and the oversight model, not with the machine. If an agent crosses an intended boundary, the failure is usually in governance design, not in the existence of the tool itself. Teams should assign ownership for policy scope, approval logic, and containment limits before deployment.
👉 Read our full editorial: Agentic AI weakens trust assumptions behind zero trust controls
