Authorization Drift in Evolving Long-Lived Agents (Privilege Grant Continuity Gap)
First seen Jul 28, 2026 · Updated Jul 28, 2026
This is an academic arXiv paper proposing a formal authorization model for AI agents that change over time (learning skills, delegating, gaining tools), addressing when a user's original permission grant should still be considered valid. It is a defensive/theoretical contribution, not a description of an active exploit or vulnerability in a deployed system, so severity is low. It is nonetheless relevant background for understanding privilege-abuse risks in evolving agentic systems.
Technical Analysis
The paper identifies a real conceptual gap: as agents mutate (via memory retention, tool acquisition, delegation, or workflow changes), the authority a user originally granted may no longer correspond to the agent's current capabilities or context, creating a path for authorization scope to silently expand ('authorization drift'). The proposed 'state-bound model' fixes an immutable effect ceiling and a transition envelope at grant time, using complete mediation, sound effect abstraction, attenuating delegation, and monitor integrity to prove that mutation cannot amplify protected effects beyond the original ceiling. This is a governance/access-control framework rather than an attack technique; it does not describe an exploited system, malicious tool, or working payload, but it does map six mutation classes that real-world agent architectures should treat as re-authorization triggers. The relevance to threat analysis is that unconstrained agent evolution (self-modifying prompts, tool acquisition, sub-agent delegation) is exactly the mechanism attackers could otherwise abuse for privilege escalation absent such controls.
Detection Signatures
- N/A - theoretical paper, no indicators of compromise.
- For defenders building agent systems: monitor for agents whose tool/capability set or delegation graph changes without a corresponding re-authorization event.
- Log and alert on privilege or effect-scope increases following agent 'skill acquisition' or memory updates that were not accompanied by explicit user re-grant.
Remediation Steps
- 1
Adopt grant-time effect ceilings
Define an immutable maximum permitted effect scope at the time authorization is issued, independent of later agent capability changes.
- 2
Re-authorize on capability mutation
Require explicit human or policy-engine re-approval when an agent acquires new tools, delegates tasks, or otherwise changes its effective capability set.
- 3
Enforce complete mediation
Route all agent actions through a policy enforcement point that checks current authority against the fixed ceiling rather than trusting agent self-reports.
- 4
Distinguish requested vs realized effects
Validate actual side effects of tool calls against declared intent to detect authority creep from agent-produced evidence.
- 5
Attenuate delegation
Ensure delegated sub-agents can only receive a subset of the delegator's authority, never an expansion of it.
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