Least-Privilege Language Models

• Least-privilege language models are systems that limit accessible computation, data, and functions to the minimum required for a specific, legitimate task.
• They enforce security via agent-level policy control, flow-sensitive access mechanisms, and internal model gating to block unauthorized privilege escalation.
• These models balance enhanced security with some loss in task utility, as demonstrated by reduced attack success rates and strict noninterference guarantees.

Least-privilege LLMs (LPLMs) are systems in which the accessible computation, data, or capabilities of a LLM are explicitly minimized—per user, request, tool call, or context—to the smallest set necessary for a specified, legitimate purpose. Enforcing the principle of least privilege (PoLP) in LLM-based agents, APIs, or model architectures aims to block privilege escalation attacks, data leakage, and abuse, while retaining high task utility and flexibility. Research on LPLMs spans agent-level policy control, compositional model construction, input/output rewriting, sensitivity-aware training, and direct interventions on the model's function class to ensure only the minimum required privileges are exercised at inference.

1. Formalization of Least Privilege in LLMs

Least privilege in LMs is defined by reference to classic security principles: for every user action, the system should provision only the minimal rights (tools, data, functions) necessary to fulfill that action, and nothing more. While the notion is straightforward in access-control systems (e.g., file systems, APIs), its practical meaning and mechanisms for LLMs are diverse:

• Tool access policies: For agentic LLMs interfacing with external tools/APIs (e.g., email, banking), least privilege is realized via explicit policy sets that grant or deny individual tool calls based on task requirements (Shi et al., 16 Apr 2025).
• Information flow: In agent architectures, privilege can be defined in terms of permissible data flows, with guardrails ensuring that untrusted or unprivileged data cannot reach high-sensitivity sinks (e.g., via flow graphs or proxies) (Kim et al., 17 Mar 2025, Ji et al., 17 Jan 2026).
• Model internals: In recent work, privilege is mapped directly to the model's reachable forward-pass computations. Privilege levels control which internal parameters or computation paths are available for a given request (Rauba et al., 30 Jan 2026).
• Representation leakage: The formal limitation of representation learning under least-privilege constraints establishes a tradeoff between task utility and the impossibility of completely suppressing inference of all unintended attributes (Stadler et al., 2024).

Summing up, the core mathematical formalization often takes the form: Let $\rho$ be the set of granted privileges, and for every function (tool call, model computation, or data access) $f$, enforce $f \in \rho$ if and only if it is minimally required for the task; all others are blocked, refused, or rendered inaccessible.

2. System Architectures and Enforcement Mechanisms

Research has produced several concrete architectures and mechanism classes to enforce least-privilege semantics in LLM-based systems.

A. Agent-Level Policy Enforcement

Systems such as Progent (Shi et al., 16 Apr 2025), SEAgent (Ji et al., 17 Jan 2026), and MiniScope (Zhu et al., 11 Dec 2025) use declarative, fine-grained policy languages or attribute-based access control frameworks:

• Policy DSLs: Developers define per-tool constraints, guard predicates, fallback behaviors, and priorities (cf. Progent’s BNF-formalized DSL). Policies are enforced in “out-of-band” SecureCall wrappers that intercept every tool invocation.
• Flow-sensitive ABAC: SEAgent maintains an evolving information flow graph over agents, tools, databases, and user actions. Policies bind to graph paths (such as agent → tool) and are evaluated as Boolean expressions over node attributes.
• Permission hierarchies: MiniScope reconstructs OAuth scope→API-method trees and solves an ILP to assign exactly the minimal scope set for agent execution plans, combined with mobile-style permission prompts.

B. Multi-Agent and Data-Type Separation

Prompt Flow Integrity (PFI) (Kim et al., 17 Mar 2025) and type-directed privilege separation (Jacob et al., 30 Sep 2025) further generalize LPLM architecture:

• Process isolation: PFI splits the agent into trusted/untrusted components, enforces clear context boundaries, and uses runtime flow checking to prevent unsafe escalation. All untrusted data interaction must be mediated via explicit, typed queries and proxies.
• Typed handoff: Restricting cross-agent or cross-stage communication to a small, safe type system (ints, enums, schema-validated JSON) eliminates the channel for prompt injection, as formalized by a non-interference theorem for type-directed privilege separation.

C. Internal Model Interventions

The "Nested Least-Privilege Networks" (NLPNs) (Rauba et al., 30 Jan 2026) intervene at the level of model weight matrices, enabling a smooth, policy-controllable restriction of model capacity:

• Low-rank gating: Every linear layer $W$ is reparameterized so that a privilege knob $g$ selects the top-$g$ singular directions. Lower $g$ strictly reduces accessible function class; the resulting model $\pi_{\theta,g}$ exposes strictly fewer internal computations.
• Monitor–allocator–enforcer: A three-level stack (signals, decision, enforcement) translates request properties into privilege settings applied inside the forward pass, with guarantees of smooth, fully reversible privilege-utility frontiers.

Enforcement Category	Representative Mechanism	Principal Security Guarantee
Policy-based agent tooling	SecureCall/DSL (Progent), MiniScope	Deterministic, deny-by-default, user-auditable
Data flow restriction	Flow graph + ABAC (SEAgent), PFI	Cross-agent, multi-turn, and multi-entity coverage
Typed interfaces	Type enrichment and handoff	Non-interference; formal elimination of injection
Internal model gating	NLPNs, privilege-indexed computation	True function-class reduction (not just output block)

3. Formal Limits and Trade-Offs of Least-Privilege Learning

The feasibility of completely isolating task-relevant information from all extraneous sensitive attributes in representations has been formally characterized as impossible except in trivial cases (Stadler et al., 2024). For a LLM encoder $f:\mathcal{X}\to \mathcal{Z}$, utility for task $Y$ (e.g. $I_\alpha(Y;Z)$) is fundamentally bounded by the maximal leakage to any attribute $S\ne Y$:

$$I_\alpha(Y;Z) \leq \sup_{S\neq Y} I_\alpha(S;Z\ | \ Y)$$

This impossibility holds regardless of architecture, adversarial censoring, or privacy regularization scheme. Empirically, even when a specific sensitive property is censored, adversaries can always find another inferred attribute whose information gain matches or exceeds utility. Thus, any practical least-privilege policy for LLMs must embrace quantifiable trade-offs: reduction in privilege (or capacity) will degrade utility, and perfect “leak nothing but $Y$” approaches do not exist in the language setting. Mitigations such as cryptographic post-processing, differential privacy, or direct privilege restriction at call or internal-computation level remain the only practical recourse.

4. Practical Deployment Regimes and Evaluation

Practical systems instantiate least-privilege in several complementary axes:

• API/plugin ecosystems: Static analysis and runtime permissioning can constrain plugin/resource privileges to match declared intentions, as with the MCP Market survey (Li et al., 5 Jul 2025) and its dynamic permission + trust-scoring proposals.
• Audio LLMs: End-to-end Audio LMs present heightened risk due to exposure of sensitive paralinguistic features. Least privilege informs architectural decisions: cascaded ASR→text LMs are preferred by default unless end-to-end modeling is strictly required for the task (He et al., 21 Mar 2025).
• User-level privacy preferences: Pipe-and-filter pipelines where user-instructed privacy profiles drive masking/paraphrasing of API queries (cf. PEEP benchmark (Ramírez et al., 7 Jul 2025)) operationalize least privilege at content redaction, with direct measures of attribute leakage and utility trade-offs.
• Role- and sensitivity-aware LLMs: Fine-tuning strategies (e.g., sudoLLM (Saha et al., 20 May 2025), sensitivity-aware LoRA (Fazlija et al., 28 Jan 2026)) embed user- or session-specific privilege signals directly into model behavior, with explicit refusal for unauthorized queries and robust resistance to prompt-based jailbreaking.
• Compositional secure model architectures: SecureLLM (Alabdulkareem et al., 2024) composes per-silo fine-tuned LLMs via logit-max or adapter-wise interventions, enforcing noninterference such that unauthorized silos cannot influence output.

Quantitative metrics in these systems include attack success rate (ASR), utility retention, policy optimality, overprivilege ratios, privilege–utility Pareto frontiers, privilege activation rates, and leakage rates (e.g., LeakPRO for privacy attributes). Empirical results demonstrate ASR reductions from >40% to <2% in agent benchmarks under privilege-enforcing regimes (Shi et al., 16 Apr 2025), 0% prompt-injection success under type-driven separation (Jacob et al., 30 Sep 2025), and strict noninterference in compositional models (Alabdulkareem et al., 2024). However, gains may be offset by increased latency, user burden (permission prompts), or mild losses in non-sensitive capability benchmarks.

5. Limitations, Open Challenges, and Future Directions

Several unresolved issues persist in least-privilege LLM research:

• Semantic mapping of privilege to internal model behaviors: For NLPN-based control (Rauba et al., 30 Jan 2026), privilege (rank or parameter access) is a proxy for capability, but its mapping to specific semantic abilities is only empirically established.
• Scalability to dynamic or fine-grained permissions: Most robust mechanisms (e.g., per-silo fine-tuning, policy DSLs) assume relatively static privilege sets. Dynamic or per-user-per-request privilege management at Internet scale remains challenging.
• Unavoidable statistical leakage: Owing to public attribute correlations, even perfect policy enforcement cannot prevent inferences based on non-sensitive context (Stadler et al., 2024, Fazlija et al., 28 Jan 2026).
• Adaptive attacks and side channels: Models trained for sensitivity awareness or least privilege may still be vulnerable to adversarial chaining, collusion, or information leakage via indirect paths unless the entire system—agents, plugins, data flows, and model computation—is comprehensively instrumented.
• User burden and usability trade-offs: Mechanisms that demand frequent user approval (e.g., per-method permission confirmation) risk fatigue unless permission hierarchies or learned-preference reductions are used (Zhu et al., 11 Dec 2025).

Future research directions include the development of scalable, fine-grained privilege management APIs; learning-based allocators for dynamic privilege ceilings; integration with formal privacy frameworks (differential privacy, information flow control); semantic disentanglement of model capabilities to match privilege sets; and open-benchmarks for measuring privilege adherence, especially in cross-modal (audio, vision, structured data) LLM deployments.

6. Representative Systems and Experimental Outcomes

A selection of notable systems and their main empirical outcomes is summarized below.

System	Approach	Notable Security/Utility Results
Progent (Shi et al., 16 Apr 2025)	Policy-DSL, SecureCall, LLM-driven	ASR drops (41→2%, 70→7%, →0%) with ≈ baseline utility
PFI (Kim et al., 17 Mar 2025)	Agent/data separation, FlowCheck	ATR reduced from 81% to 0%, utility 62–68%
MiniScope (Zhu et al., 11 Dec 2025)	Hierarchical ILP + runtime prompt	Overprivilege ratio 1.04–2.19 (LLMs up to 2×), 1–6% latency overhead
MCP Static Analysis (Li et al., 5 Jul 2025)	Static code audit, dynamic permission proposal	Over 1,237 servers with excessive system API use; proposes dynamic permission models
sudoLLM (Saha et al., 20 May 2025)	Privilege-biased query, BFT tuning	Up to +73% alignment gain, >10× lower jailbreak ASR
Type-Directed Separation (Jacob et al., 30 Sep 2025)	Typed agent split, non-interference	ASR drops to 0%, utility preserved except on rich-context tasks
Sensitivity-aware LoRA (Fazlija et al., 28 Jan 2026)	LoRA, RBAC-style finetuning	Correctness +21.7 pp (55→77%), small loss on open-domain reasoning
SecureLLM (Alabdulkareem et al., 2024)	Siloed fine-tuning, logit-max comp.	Noninterference, compositional SQL accuracy
NLPN (Rauba et al., 30 Jan 2026)	Internal low-rank gating, allocation	Privilege–utility curve, reversible suppression of capabilities

These results collectively establish both the practical effectiveness and the unavoidable trade-offs in LPLM system design: strong privilege controls yield dramatic reductions in attack surface but may degrade utility or require additional engineering effort.

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In summary, least-privilege LLMs encompass a spectrum of rigorously defined, empirically validated architectural and algorithmic approaches, targeting the principle that LLMs, agents, and their integrations expose only the minimal necessary capabilities to fulfill user intent. Enforced via policy control, type systems, information flow, and direct modulation of model computation, LPLMs are essential for secure deployment in sensitive, extensible, and multi-agent environments. The field is defined by both its theoretical limits and its rapidly advancing deployment techniques.