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ConceptPoLP / Principle of Least Privilege

Principle of Least Privilege

A foundational principle in information security stating that every subject in a system should be granted only the minimum permissions necessary to perform its designated tasks. The principle was formally articulated by Jerome Saltzer and Michael Schroeder in 1975.

Core Facts

Timeline (last 90 days)

Oct 21

In AI agent scenarios, least privilege needs to shift to runtime enforcement, i.e., re-verifying the initiator's identity and the target resource before every tool call.

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Oct 21

The principle of least privilege requires requesting only the scopes the business truly needs; the narrower the scopes, the smaller the blast radius of a token leak or misuse.

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Oct 21

Practical implications for development teams include: prioritizing reachable vulnerabilities and establishing traceable exemption records for unreachable alerts, incorporating the tools used by agents into software supply chain management, and configuring agent tools according to the principle of least privilege.

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Oct 21

To implement the principle of least privilege in agent scenarios, the available tools and target address ranges should be dynamically narrowed per session or task, and actual connections and access requests should be re-verified at the infrastructure layer.

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Oct 20

Governing privilege creep typically relies on regular access reviews and the least privilege principle.

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Oct 20

The principle of least privilege is the cornerstone of access control: each account should only have the minimum permissions necessary to complete its current work, and only for as long as necessary.

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Oct 20

The industry-standard approach to addressing data sensitivity is the "principle of least privilege," whereby each integrating party can access only the minimal dataset necessary to complete a specific task.

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Oct 20

The permission recommendation for resident agents is: authorize on demand rather than opening everything at once—for example, if it can read a specific folder, don't give it the entire disk; if it can check the calendar, don't attach write permission; if it can view market quotes, don't bind a trading account.

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Oct 19

During token exchange, the scope should be actively narrowed rather than carrying over all upstream permissions, and this should be combined with short validity periods and audience restrictions so that the credential can only be used by a specific recipient within a specific time window.

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Oct 19

When delegating work to an external execution-oriented assistant, the industry typically adopts the principle of least privilege: granting only the access scope necessary to complete the current task and setting manual confirmation for sensitive operations.

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40 more timeline events

All Facts (20)

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The Principle of Least Privilege holds that any principal should only be granted the minimum permissions necessary to complete its task.

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The Principle of Least Privilege (PoLP) was formally proposed by Jerome Saltzer in 1975, requiring that each component in a system be granted only the minimum set of permissions necessary to complete its task.

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The Principle of Least Privilege (PoLP) originates from the field of computer security in the 1970s and was proposed by Jerome Saltzer at MIT.

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The principle of least privilege was proposed by Jerome Saltzer in 1975.

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AI Agent具备'规划-执行-反馈'的完整闭环能力,能自主拆解复杂任务、调用外部工具、持续迭代直到任务完成

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The Principle of Least Privilege is a foundational principle in information security, requiring that system components possess only the minimum permissions necessary to complete the current task.

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The Principle of Least Privilege (POLP) requires that any principal possess, at any moment, only the minimum set of permissions necessary to complete the current task.

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Common protective measures against AI agents include input filtering, least-privilege access, sandbox isolation, and behavioral monitoring.

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Agents should be granted minimal privileges—for example, an agent that only needs to read logs should not have the ability to write to the database—and the clearer the permission boundaries, the more controllable the scope of damage in the event of a loss of control

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Every expansion of AI capabilities is accompanied by an enlargement of the attack surface, which is essentially a trade-off between convenience and security.

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Multi-agent systems exhibit non-stationarity, as the optimal policy for each agent depends on the behavior of the other agents.

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The research community has proposed solutions such as the "principle of least privilege," Human-in-the-Loop approval mechanisms, and sandboxed isolated execution environments to mitigate AI Agent security risks.

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Security control solutions for AI agents include the principle of least privilege, capability tokens (temporary authorization with time and scope restrictions), and tiered approval mechanisms.

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Platforms replacing human review with AI are cost-efficient, but when AI makes mistakes users find themselves in a situation with no one to appeal to, creating an inherent tension between transparency and security.

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The execution paths of AI agents are highly dynamic, making it difficult to statically declare all permission requirements before startup, and the traditional principle of least privilege is difficult to apply mechanically.

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The principle of least privilege requires that every user, program, or service within a system be granted only the minimum access permissions necessary to perform their work.

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The principle of least privilege is considered one of the core ideas in agent security, meaning an agent should only be granted the minimum permissions necessary to complete its task.

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The Principle of Least Privilege (PoLP) is a foundational tenet of information security spanning decades, stipulating that any system component should only be granted the minimum permissions necessary to complete its current task.

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Applying the principle of least privilege to AI agents means that agents should not have network access by default, and should only temporarily gain it when the user explicitly authorizes a specific request.

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Without human guidance, even with strongly configured system prompts, AI agent behavior tends toward two extremes: rapid polarization or polite consensus loops.

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