Team Hedgehogs' Data Observability Dashboard by gsamardzhiev

Description

This project aims to develop a comprehensive Data Observability Dashboard that provides r insights into key aspects of data quality and reliability. The dashboard will track:

Data Freshness: Monitor when data was last updated and flag potential delays.

Data Volume: Track table row counts to detect unexpected surges or drops in data.

Data Distribution: Analyze data for null values, outliers, and anomalies to ensure accuracy.

Data Schema: Track schema changes over time to prevent breaking changes.

The dashboard's aim is to support historical tracking to support proactive data management and enhance data trust across the data function.

Goals

Although the final goal is to create a power bi dashboard that we are able to monitor, our goals is to 1. Create the necessary tables that track the relevant metadata about our current data 2. Automate the process so it runs in a timely manner

Resources

AWS Redshift; AWS Glue, Airflow, Python, SQL

Why Hedgehogs?

Because we like them.

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Ansible for add-on management by lmanfredi

Description

Machines can contains various combinations of add-ons and are often modified during the time.

The list of repos can change so I would like to create an automation able to reset the status to a given state, based on metadata available for these machines

Goals

Create an Ansible automation able to take care of add-on (repo list) configuration using metadata as reference

Resources

• Machines
• Repositories
• Developing modules
• Basic VM Guest management
• Module zypper_repository_list
• ansible-collections community.general
  • plugins/modules/zypper.py
  • plugins/modules/zypper_repository.py
  • plugins/modules/zypperrepositoryinfo.py

Results

Created WIP project Ansible-add-on-openSUSE

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Make more sense of openQA test results using AI by livdywan

Description

AI has the potential to help with something many of us spend a lot of time doing which is making sense of openQA logs when a job fails.

User Story

Allison Average has a puzzled look on their face while staring at log files that seem to make little sense. Is this a known issue, something completely new or maybe related to infrastructure changes?

Goals

• Leverage a chat interface to help Allison
• Create a model from scratch based on data from openQA
• Proof of concept for automated analysis of openQA test results

Bonus

• Use AI to suggest solutions to merge conflicts
  • This would need a merge conflict editor that can suggest solving the conflict
• Use image recognition for needles

Resources

• TensorFlow
• open-webui
• openQA instance of openSUSE
• openQA documentation

Timeline

Day 1

• Conversing with open-webui to teach me how to create a model based on openQA test results
  • Asking for example code using TensorFlow in Python
  • Discussing log files to explore what to analyze
  • Drafting a new project called Testimony (based on Implementing a containerized Python action) - the project name was also suggested by the assistant

Day 2

• Using NotebookLLM (Gemini) to produce conversational versions of blog posts
  • Writing openQA tests in Python
  • Developing in distrobox containers
• Researching the possibility of creating a project logo with AI
  • Asking open-webui, persons with prior experience and conducting a web search for advice

Highlights

• I briefly tested compared models to see if they would make me more productive. Between llama, gemma and mistral there was no amazing difference in the results for my case.
• Convincing the chat interface to produce code specific to my use case required very explicit instructions.
• Asking for advice on how to use open-webui itself better was frustratingly unfruitful both in trivial and more advanced regards.
• Documentation on source materials used by LLM's and tools for this purpose seems virtually non-existent - specifically if a logo can be generated based on particular licenses

Outcomes

• Chat interface-supported development is providing good starting points and open-webui being open source is more flexible than Gemini. Although currently some fancy features such as grounding and generated podcasts are missing.
• Allison still has to be very experienced with openQA to use a chat interface for test review. Publicly available system prompts would make that easier, though.

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Symbol Relations by hli

Description

There are tools to build function call graphs based on parsing source code, for example, cscope.

This project aims to achieve a similar goal by directly parsing the disasembly (i.e. objdump) of a compiled binary. The assembly code is what the CPU sees, therefore more "direct". This may be useful in certain scenarios, such as gdb/crash debugging.

Detailed description and Demos can be found in the README file:

Supports x86 for now (because my customers only use x86 machines), but support for other architectures can be added easily.

Tested with python3.6

Goals

Any comments are welcome.

Resources

https://github.com/lhb-cafe/SymbolRelations

symrellib.py: mplements the symbol relation graph and the disassembly parser

symrel_tracer*.py: implements tracing (-t option)

symrel.py: "cli parser"

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SUSE AI Meets the Game Board by moio

Use tabletopgames.ai’s open source TAG and PyTAG frameworks to apply Statistical Forward Planning and Deep Reinforcement Learning to two board games of our own design. On an all-green, all-open source, all-AWS stack!

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Results: Infrastructure Achievements

We successfully built and automated a containerized stack to support our AI experiments. This included:

• a Fully-Automated, One-Command, GPU-accelerated Kubernetes setup: we created an OpenTofu based script, tofu-tag, to deploy SUSE's RKE2 Kubernetes running on CUDA-enabled nodes in AWS, powered by openSUSE with GPU drivers and gpu-operator
• Containerization of the TAG and PyTAG frameworks: TAG (Tabletop AI Games) and PyTAG were patched for seamless deployment in containerized environments. We automated the container image creation process with GitHub Actions. Our forks (PRs upstream upcoming):
  • https://github.com/moio/TabletopGames/tree/hackweek24
  • https://github.com/moio/PyTAG/tree/hackweek24

./deploy.sh and voilà - Kubernetes running PyTAG (k9s, above) with GPU acceleration (nvtop, below)

Results: Game Design Insights

Our project focused on modeling and analyzing two card games of our own design within the TAG framework:

• Game Modeling: We implemented models for Dario's "Bamboo" and Silvio's "Totoro" and "R3" games, enabling AI agents to play thousands of games ...in minutes!
• AI-driven optimization: By analyzing statistical data on moves, strategies, and outcomes, we iteratively tweaked the game mechanics and rules to achieve better balance and player engagement.
• Advanced analytics: Leveraging AI agents with Monte Carlo Tree Search (MCTS) and random action selection, we compared performance metrics to identify optimal strategies and uncover opportunities for game refinement .
  • more about Bamboo on Dario's site
  • more about R3 on Silvio's site (italian, translation coming)
  • more about Totoro on Silvio's site

A family picture of our card games in progress. From the top: Bamboo, Totoro, R3

Results: Learning, Collaboration, and Innovation

Beyond technical accomplishments, the project showcased innovative approaches to coding, learning, and teamwork:

• "Trio programming" with AI assistance: Our "trio programming" approach—two developers and GitHub Copilot—was a standout success, especially in handling slightly-repetitive but not-quite-exactly-copypaste tasks. Java as a language tends to be verbose and we found it to be fitting particularly well.
• AI tools for reporting and documentation: We extensively used AI chatbots to streamline writing and reporting. (Including writing this report! ...but this note was added manually during edit!)
• GPU compute expertise: Overcoming challenges with CUDA drivers and cloud infrastructure deepened our understanding of GPU-accelerated workloads in the open-source ecosystem.
• Game design as a learning platform: By blending AI techniques with creative game design, we learned not only about AI strategies but also about making games fun, engaging, and balanced.

Last but not least we had a lot of fun! ...and this was definitely not a chatbot generated line!

The Context: AI + Board Games

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VulnHeap by r1chard-lyu

Description

The VulnHeap project is dedicated to the in-depth analysis and exploitation of vulnerabilities within heap memory management. It focuses on understanding the intricate workflow of heap allocation, chunk structures, and bin management, which are essential to identifying and mitigating security risks.

Goals

• Familiarize with heap
  • Heap workflow
  • Chunk and bin structure
  • Vulnerabilities
• Vulnerability
  • Use after free (UAF)
  • Heap overflow
  • Double free
• Use Docker to create a vulnerable environment and apply techniques to exploit it

Resources

• https://heap-exploitation.dhavalkapil.com/divingintoglibc_heap
• https://raw.githubusercontent.com/cloudburst/libheap/master/heap.png
• https://github.com/shellphish/how2heap?tab=readme-ov-file

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CVE portal for SUSE Rancher products by gmacedo

Description

Currently it's a bit difficult for users to quickly see the list of CVEs affecting images in Rancher, RKE2, Harvester and Longhorn releases. Users need to individually look for each CVE in the SUSE CVE database page - https://www.suse.com/security/cve/ . This is not optimal, because those CVE pages are a bit hard to read and contain data for all SLE and BCI products too, making it difficult to easily see only the CVEs affecting the latest release of Rancher, for example. We understand that certain costumers are only looking for CVE data for Rancher and not SLE or BCI.

Goals

The objective is to create a simple to read and navigate page that contains only CVE data related to Rancher, RKE2, Harvester and Longhorn, where it's easy to search by a CVE ID, an image name or a release version. The page should also provide the raw data as an exportable CSV file.

It must be an MVP with the minimal amount of effort/time invested, but still providing great value to our users and saving the wasted time that the Rancher Security team needs to spend by manually sharing such data. It might not be long lived, as it can be replaced in 2-3 years with a better SUSE wide solution.

Resources

• The page must be simple and easy to read.
• The UI/UX must be as straightforward as possible with minimal visual noise.
• The content must be created automatically from the raw data that we already have internally.
• It must be updated automatically on a daily basis and on ad-hoc runs (when needed).
• The CVE status must be aligned with VEX.
• The raw data must be exportable as CSV file.
• Ideally it will be written in Go or pure Shell script with basic HTML and no external dependencies in CSS or JS.

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Contributing to Linux Kernel security by pperego

Description

A couple of weeks ago, I found this blog post by Gustavo Silva, a Linux Kernel contributor.

I always strived to start again into hacking the Linux Kernel, so I asked Coverity scan dashboard access and I want to contribute to Linux Kernel by fixing some minor issues.

I want also to create a Linux Kernel fuzzing lab using qemu and syzkaller

Goals

1. Fix at least 2 security bugs
2. Create the fuzzing lab and having it running

The story so far

• Day 1: setting up a virtual machine for kernel development using Tumbleweed. Reading a lot of documentation, taking confidence with Coverity dashboard and with procedures to submit a kernel patch
• Day 2: I read really a lot of documentation and I triaged some findings on Coverity SAST dashboard. I have to confirm that SAST tool are great false positives generator, even for low hanging fruits.
• Day 3: Working on trivial changes after I read this blog post: https://www.toblux.com/posts/2024/02/linux-kernel-patches.html. I have to take confidence with the patch preparation and submit process yet.
  • First trivial patch sent: using strtruefalse() macro instead of hard-coded strings in a staging driver for a lcd display
  • Fix for a dereference before null check issue discovered by Coverity (CID 1601566) https://scan7.scan.coverity.com/#/project-view/52110/11354?selectedIssue=1601566
• Day 4: Triaging more issues found by Coverity.
  • The patch for CID 1601566 was refused. The check against the NULL pointer was pointless so I prepared a version 2 of the patch removing the check.
  • Fixed another dereference before NULL check in iwlmvmparsewowlaninfo_notif() routine (CID 1601547). This one was already submitted by another kernel hacker :(
• Day 5: Wrapping up. I had to do some minor rework on patch for CID 1601566. I found a stalker bothering me in private emails and people I interacted with me, advised he is a well known bothering person. Markus Elfring for the record.

• Wrapping up: being back doing kernel hacking is amazing and I don't want to stop it. My battery pack is completely drained but changing the scope gave me a great twist and I really want to feel this energy not doing a single task for months.

  I failed in setting up a fuzzing lab but I was too optimistic for the patch submission process.

The patches

1

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OIDC Loginproxy by toe

Description

Reverse proxies can be a useful option to separate authentication logic from application logic. SUSE and openSUSE use "loginproxies" as an authentication layer in front of several services.

Currently, loginproxies exist which support LDAP authentication or SAML authentication.

Goals

The goal of this Hack Week project is, to create another loginproxy which supports OpenID Connect authentication which can then act as a drop-in replacement for the existing LDAP or SAML loginproxies.

Testing is intended to focus on the integration with OIDC IDPs from Okta, KanIDM and Authentik.

Resources

• #122254: Migrate away from login proxies to SSO

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Model checking the BPF verifier by shunghsiyu

Project Description

BPF verifier plays a crucial role in securing the system (though less so now that unprivileged BPF is disabled by default in both upstream and SLES), and bugs in the verifier has lead to privilege escalation vulnerabilities in the past (e.g. CVE-2021-3490).

One way to check whether the verifer has bugs to use model checking (a formal verification technique), in other words, build a abstract model of how the verifier operates, and then see if certain condition can occur (e.g. incorrect calculation during value tracking of registers) by giving both the model and condition to a solver.

For the solver I will be using the Z3 SMT solver to do the checking since it provide a Python binding that's relatively easy to use.

Goal for this Hackweek

Learn how to use the Z3 Python binding (i.e. Z3Py) to build a model of (part of) the BPF verifier, probably the part that's related to value tracking using tristate numbers (aka tnum), and then check that the algorithm work as intended.

Resources

• Formal Methods for the Informal Engineer: Tutorial #1 - The Z3 Theorem Prover and its accompanying notebook is a great introduction into Z3
  • Has a section specifically on model checking
• Software Verification and Analysis Using Z3 a great example of using Z3 for model checking
• Sound, Precise, and Fast Abstract Interpretation with Tristate Numbers - existing work that use formal verification to prove that the multiplication helper used for value tracking work as intended
• [PATCH v5 net-next 00/12] bpf: rewrite value tracking in verifier - initial patch set that adds tristate number to the verifier

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Drag Race - comparative performance testing for pull requests by balanza

Description

«Sophia, a backend developer, submitted a pull request with optimizations for a critical database query. Once she pushed her code, an automated load test ran, comparing her query against the main branch. Moments later, she saw a new comment automatically added to her PR: the comparison results showed reduced execution time and improved efficiency. Smiling, Sophia messaged her team, “Performance gains confirmed!”»

Goals

• To have a convenient and ergonomic framework to describe test scenarios, including environment and seed;
• to compare results from different tests
• to have a GitHub action that executes such tests on a CI environment

Resources

The MVP will be built on top of Preevy and K6.

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