SysComb: Fine-Grained Transparent System Call Filtering for Attack Surface Reduction
- URL: http://arxiv.org/abs/2608.26871v1
- Date: Thu, 27 Aug 2026 09:34:10 GMT
- Title: SysComb: Fine-Grained Transparent System Call Filtering for Attack Surface Reduction
- Abstract summary: SysComb is a novel eBPF-based solution to enforce temporally-specialized system call filters based on the application state.<n> SysComb lets the developer choose between two distinct enforcement strategies: seccomp-like, ensuring no new privileges are gained after a state transition is performed, and least-privilege, which applies to each state the most restrictive filter.
- Score: 1.3480777640269428
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Restricting the system calls available to applications shrinks the kernel's attack surface and greatly mitigates the impact of compromised programs. Recent approaches showcase techniques to generate system call filters, however, all existing solutions require either kernel or application modifications to activate them at runtime. This is intrusive, error-prone, and often impractical, especially when the code is maintained by external parties. This paper presents SysComb, a novel eBPF-based solution to enforce temporally-specialized system call filters based on the application state, without requiring any modification to the application or the kernel code, and thus addressing the above limitations. Moreover, SysComb lets the developer choose between two distinct enforcement strategies: seccomp-like, ensuring no new privileges are gained after a state transition is performed, and least-privilege, which applies to each state the most restrictive filter. We evaluated SysComb using widely used software, showcasing accurate state-aware system call filtering and an overhead comparable to built-in kernel solutions, demonstrating the practicality of our approach.
Related papers
- A Self-Triggered Agentic Push Recommendation System [77.13502692161426]
Push notification is a critical recommendation scenario on large-scale platforms.<n>We propose a proactive, Self-Triggered End-to-end Agentic Push Recommendation System.<n>STEPS is already fully deployed at Douyin with over 1 billion users.
arXiv Detail & Related papers (2026-08-03T09:20:53Z) - Aligning Flow Map Policies with Optimal Q-Guidance [50.514994916864275]
Flow map policies are designed for fast action generation by learning to take arbitrary-size jumps.<n>We derive FLOW MAP Q-GUIDANCE (FMQ), a principled closed-form learning target that is optimal for adapting offline flow map policies under a critic-guided trust-region constraint.<n>FMQ achieves state-of-the-art performance in offline-to-online RL, outperforming the previous one-step policy MVP by a relative improvement of 21.3% on the average success rate.
arXiv Detail & Related papers (2026-05-12T17:12:29Z) - OAMAC: Origin-Aware Mandatory Access Control for Practical Post-Compromise Attack Surface Reduction [0.0]
Execution origin is a missing abstraction in modern operating system security models.<n>We introduce origin-aware mandatory access control (OAMAC)<n> OAMAC treats execution origin as a first-class security attribute.
arXiv Detail & Related papers (2026-01-20T14:40:26Z) - eBPF-PATROL: Protective Agent for Threat Recognition and Overreach Limitation using eBPF in Containerized and Virtualized Environments [0.23332469289621782]
eBPF-PATROL is a lightweight runtime security agent that monitors and enforces policies in containerized environments.<n>eBPF-PATROL detects and prevents real-time boundary violations, such as reverse shells, privilege escalation, and container escape attempts.
arXiv Detail & Related papers (2025-11-22T18:51:36Z) - Making 'syscall' a Privilege not a Right [4.674007120771649]
nexpoline is a secure syscall interception mechanism combining Memory Protection Keys (MPK) and Seccomp or Syscall User Dispatch (SUD)
It offers better efficiency than secure interception techniques like ptrace, as nexpoline can intercept syscalls through binary rewriting securely.
Notably, it operates without kernel modifications, making it viable on current Linux systems without needing root privileges.
arXiv Detail & Related papers (2024-06-11T16:33:56Z) - SYSPART: Automated Temporal System Call Filtering for Binaries [4.445982681030902]
Recent approaches automatically identify the system calls required by programs to block unneeded ones.
SYSPART is an automatic system-call filtering system designed for binary-only server programs.
arXiv Detail & Related papers (2023-09-10T23:57:07Z) - Sparsity-Aware Intelligent Massive Random Access Control in Open RAN: A
Reinforcement Learning Based Approach [61.74489383629319]
Massive random access of devices in the emerging Open Radio Access Network (O-RAN) brings great challenge to the access control and management.
reinforcement-learning (RL)-assisted scheme of closed-loop access control is proposed to preserve sparsity of access requests.
Deep-RL-assisted SAUD is proposed to resolve highly complex environments with continuous and high-dimensional state and action spaces.
arXiv Detail & Related papers (2023-03-05T12:25:49Z) - Distributed-Training-and-Execution Multi-Agent Reinforcement Learning
for Power Control in HetNet [48.96004919910818]
We propose a multi-agent deep reinforcement learning (MADRL) based power control scheme for the HetNet.
To promote cooperation among agents, we develop a penalty-based Q learning (PQL) algorithm for MADRL systems.
In this way, an agent's policy can be learned by other agents more easily, resulting in a more efficient collaboration process.
arXiv Detail & Related papers (2022-12-15T17:01:56Z) - SCOPE: Safe Exploration for Dynamic Computer Systems Optimization [18.498208917123414]
We present SCOPE, a resource manager that dynamically allocates hardware resources from the execution space.
We evaluate SCOPE's ability to deliver improved latency while minimizing power constraint violations.
arXiv Detail & Related papers (2022-04-22T00:58:52Z) - Real-Time GPU-Accelerated Machine Learning Based Multiuser Detection for
5G and Beyond [70.81551587109833]
nonlinear beamforming filters can significantly outperform linear approaches in stationary scenarios with massive connectivity.
One of the main challenges comes from the real-time implementation of these algorithms.
This paper explores the acceleration of APSM-based algorithms through massive parallelization.
arXiv Detail & Related papers (2022-01-13T15:20:45Z) - Adaptive Low-Pass Filtering using Sliding Window Gaussian Processes [71.23286211775084]
We propose an adaptive low-pass filter based on Gaussian process regression.
We show that the estimation error of the proposed method is uniformly bounded.
arXiv Detail & Related papers (2021-11-05T17:06:59Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.