PrivEscalate: Measuring and Augmenting the Threat of LLM-Automated Linux Privilege Escalation

📅 2026-09-08
📈 Citations: 0
Influential: 0
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🤖 AI Summary
本文通过构建包含531个Docker化场景的PrivEscalate基准,评估了六种LLM在Linux提权任务中的表现,并提出PrivEscAgent以增强性能。
📝 Abstract
As Large Language Model (LLM) agents increasingly automate offensive operations across the cyber kill chain, their efficacy in complex local post-exploitation tasks remains inadequately quantified. Among these, Linux privilege escalation is a key step between initial access and full system compromise. However, existing evaluations for this task are limited by small sample sizes (fewer than 15 scenarios), lacking the scale to compare model capabilities under executable verification. To address this, we present PrivEscalate, a large-scale benchmark for Linux privilege escalation, comprising 531 Dockerized scenarios spanning 14 sub-categories. We additionally derive 329 parameterized variants to measure sensitivity to environmental distractors. Evaluating six LLMs across three agent architectures reveals: (i) model capability is heterogeneous across vulnerability classes, with no single model dominating across the high-prevalence classes, motivating multi-dimensional risk assessments; (ii) LLM successes are sensitive to environmental perturbation, so configuration rotation can disrupt some exploit attempts but does not eliminate the measured risk; and (iii) agent architectures can materially change success rates and reorder model rankings, though the magnitude is model-dependent. Leveraging these insights, we develop PrivEscAgent, a domain-specialized wrapper that augments a generic ReAct agent with deterministic enumeration, category matching, and step planning. PrivEscAgent improves over prior Linux privilege-escalation agent baselines without underlying LLM modifications. We release PrivEscalate as an open-source, Dockerized measurement instrument supporting LLM agent evaluation, defensive tool validation, and red-team training.
Problem

Research questions and friction points this paper is trying to address.

Large Language Model
Linux Privilege Escalation
Benchmark
Evaluation
Agent Architectures
Innovation

Methods, ideas, or system contributions that make the work stand out.

PrivEscalate
Linux Privilege Escalation
LLM Evaluation
PrivEscAgent
Agent Architecture
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