Search Results for "black box"
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Showing 116 open source projects for "black box"
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NeuroSploit
NeuroSploit is an advanced, AI-powered penetration testing framework
...The system performs reconnaissance, selects specialized agents for the discovered attack surface, and runs applicable assessments in parallel. Candidate findings are checked through cross-model validation and supporting tool evidence before being reported. It supports black-box, white-box, gray-box, infrastructure, and AI application security testing modes. NeuroSploit also provides a terminal interface, project memory, configurable model providers, structured reports, and a large library of specialized security agents. -
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uqlm
Uncertainty Quantification for Language Models, is a Python package
...The system implements a variety of uncertainty quantification techniques that assign confidence scores to model responses. These scores help developers determine how likely a generated answer is to contain errors or fabricated information. The library includes both black-box and white-box approaches to uncertainty estimation. Black-box methods evaluate model outputs through multiple generations or comparative analysis, while white-box methods rely on token probabilities produced during inference. UQLM also supports ensemble strategies and model-as-judge approaches for evaluating responses. By combining multiple uncertainty metrics, the system provides more reliable indicators of when language model outputs may be unreliable. -
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frida
Dynamic instrumentation toolkit for developers
Dynamic instrumentation toolkit for developers, reverse-engineers, and security researchers. Inject your own scripts into black box processes. Hook any function, spy on crypto APIs or trace private application code, no source code needed. Edit, hit save, and instantly see the results. All without compilation steps or program restarts. Works on Windows, macOS, GNU/Linux, iOS, Android, and QNX. Install the Node.js bindings from npm, grab a Python package from PyPI, or use Frida through its Swift bindings, .NET bindings, Qt/Qml bindings, or C API. ... -
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promptmap2
A security scanner for custom LLM applications
promptmap is an automated security scanner for custom LLM applications that focuses on prompt injection and related attack classes. The project supports both white-box and black-box testing, which means it can either run tests directly against a known model and system prompt configuration or attack an external HTTP endpoint without internal access. Its scanning workflow uses a dual-LLM architecture in which one model acts as the target being tested and another acts as a controller that evaluates whether an attack succeeded. ... -
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Honggfuzz
Security oriented software fuzzer
...Instrumentation via compiler hooks or hardware/perf counters guides mutations toward previously unseen edges, while persistent mode keeps the target process alive to amortize startup costs. The tool integrates tightly with sanitizers and can attach to already running processes, making it convenient for both white-box and black-box fuzzing. When it finds a crash, honggfuzz captures detailed context, minimizes the input, and can pin reproducibility by controlling CPU affinity and resource limits. Its small footprint and straightforward CLI make it easy to drop into CI or long-running campaigns across many cores. -
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WPScan
WPScan WordPress security scanner
WPScan is a black-box WordPress vulnerability scanner written in Ruby. It analyzes WordPress sites to identify outdated core, plugins, themes, exposed APIs, and known vulnerabilities using a large built-in vulnerability database. It is a popular security auditing tool for pentesters and site administrators. -
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CounterfactualExplanations.jl
A package for Counterfactual Explanations and Algorithmic Recourse
CounterfactualExplanations.jl is a package for generating Counterfactual Explanations (CE) and Algorithmic Recourse (AR) for black-box algorithms. Both CE and AR are related tools for explainable artificial intelligence (XAI). While the package is written purely in Julia, it can be used to explain machine learning algorithms developed and trained in other popular programming languages like Python and R. See below for a short introduction and other resources or dive straight into the docs. -
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Open Source Vizier
Python-based research interface for blackbox
Open Source (OSS) Vizier is a Python-based interface for blackbox optimization and research, based on Google’s original internal Vizier, one of the first hyperparameter tuning services designed to work at scale. Allows a user to setup an OSS Vizier Server, which can host black-box optimization algorithms to serve multiple clients simultaneously in a fault-tolerant manner to tune their objective functions. Defines abstractions and utilities for implementing new optimization algorithms for research and to be hosted in the service. A wide collection of objective functions and methods to benchmark and compare algorithms. ... -
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InterpretML
Fit interpretable models. Explain blackbox machine learning
...InterpretML is an open-source package that incorporates state-of-the-art machine-learning interpretability techniques under one roof. With this package, you can train interpretable glass box models and explain black box systems. InterpretML helps you understand your model's global behavior, or understand the reasons behind individual predictions. -
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BayesianOptimization
A Python implementation of global optimization with gaussian processes
BayesianOptimization is a Python library that helps find the maximum (or minimum) of expensive or unknown objective functions using Bayesian optimization. This technique is especially useful for hyperparameter tuning in machine learning, where evaluating the objective function is costly. The library provides an easy-to-use API for defining bounds and optimizing over parameter spaces using probabilistic models like Gaussian Processes. -
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JuliaFormatter.jl
An opinionated code formatter for Julia
Width-sensitive formatter for Julia code. Inspired by gofmt, refmt, black, and prettier. Built with CSTParser. Sane defaults out of the box with options to customize. Supports YAS, Blue and SciML style guides. JuliaFormatter.toml configuration file to store options. JuliaFormatter exports format, format_file, format_text, and format_md. format_md has the same API as format_text but differ in that format_md expects the text content to be a Markdown document. -
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ExplainableAI.jl
Explainable AI in Julia
This package implements interpretability methods for black box models, with a focus on local explanations and attribution maps in input space. It is similar to Captum and Zennit for PyTorch and iNNvestigate for Keras models. Most of the implemented methods only require the model to be differentiable with Zygote. Layerwise Relevance Propagation (LRP) is implemented for use with Flux.jl models. -
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Detox
Gray box end-to-end testing and automation framework for mobile apps
...The most difficult part of automated testing on mobile is the tip of the testing pyramid - E2E. The core problem with E2E tests is flakiness, tests are usually not deterministic. We believe the only way to tackle flakiness head on is by moving from black box testing to gray box testing. That's where Detox comes into play. Detox is built from the ground up to support React Native projects as well as pure native ones. Read the Getting Started Guide to get Detox running on your app in less than 10 minutes. We believe that the only way to address the core difficulties with mobile end-to-end testing is by rethinking some of the principles of the entire approach. -
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Machine Learning Octave
MatLab/Octave examples of popular machine learning algorithms
...Implementations of supervised learning algorithms (linear regression, logistic regression, neural nets). The author’s goal is to help users understand how each algorithm works “from scratch,” avoiding black-box library calls. Code written so as to expose and comment on mathematical steps. The repository includes clustering, regression, classification, neural networks, anomaly detection, and other standard ML topics. Does not rely heavily on specialized toolboxes or library shortcuts. -
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EvoTorch
Advanced evolutionary computation library built on top of PyTorch
EvoTorch is an evolutionary optimization framework built on top of PyTorch, developed by NNAISENSE. It is designed for large-scale optimization problems, particularly those that require evolutionary algorithms rather than gradient-based methods. -
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AI Engineer Coach
Better agentic engineering
...The project is read-only and emphasizes that data stays on the user’s machine. Its main value is helping developers measure and improve their AI-assisted coding process instead of treating agent use as an untracked black box. -
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Monibuca
Monibuca is a Modularized, Extensible framework for building Streaming
...Monibuca supports distributed scenarios and is built to handle large-scale streaming workloads with configurable modules. It is especially useful for developers who want a media server foundation that can be extended instead of a fixed black-box streaming product. -
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PentestAgent
AI agent framework for black-box security testing
PentestAgent is an open-source autonomous security testing platform designed to help organizations identify vulnerabilities and assess security posture by simulating real-world attack scenarios without manual intervention. It brings a modular and automated approach to penetration testing by orchestrating a suite of tools and scripts that can emulate common exploitation techniques, reconnaissance workflows, and post-exploitation activities across targets. Users configure rules, policies, and... -
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CocoIndex
ETL framework to index data for AI, such as RAG
...CocoIndex leverages vector embeddings and integrates with various models and frameworks, including OpenAI and Hugging Face, to provide high-quality semantic understanding. It’s built for transparency, ease of use, and local control over your search data, distinguishing itself from closed, black-box systems. The tool is suitable for developers working on personal knowledge bases, AI search interfaces, or private LLM applications. -
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OpenClaw Control Center
Turn OpenClaw from a black box into a local control center
OpenClaw Control Center is a centralized management interface designed to oversee, configure, and monitor agent-based systems, particularly those built within the OpenClaw ecosystem. It provides a control layer that allows users to interact with agents, track their performance, and adjust operational parameters in real time. The system is likely built with usability in mind, offering dashboards or visualization tools that make complex agent behaviors easier to understand and manage. It may... -
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Face Alignment
2D and 3D Face alignment library build using pytorch
...While not required, for optimal performance(especially for the detector) it is highly recommended to run the code using a CUDA-enabled GPU. While here the work is presented as a black box, if you want to know more about the intrisecs of the method please check the original paper either on arxiv or my webpage. -
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bilibili-API-collect
Community-maintained documentation project
bilibili-API-collect was a community-maintained documentation project that cataloged Bilibili interfaces used by its web, mobile, and TV clients. It researched undocumented endpoints through techniques such as black-box testing, traffic inspection, application analysis, and controlled experimentation. The documentation covered REST, gRPC, and WebSocket interfaces along with request structures, response formats, authentication concepts, errors, and platform-specific behavior. Major sections documented users, login flows, messaging, media, comments, live streaming, search, relationships, and other site functions. ... -
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LLM From Scratch
Build and train a GPT-style language model
...The repository is intentionally simplified to focus on conceptual clarity, using a compact model of roughly 10 million parameters that can train on consumer hardware such as laptops within a relatively short time. Inspired by Andrej Karpathy’s nanoGPT, the project emphasizes learning through direct implementation and experimentation rather than black-box usage. The workshop documentation explains concepts such as self-attention, embeddings, gradient clipping, optimizer scheduling, and decoding strategies in a practical and approachable way. -
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Nevergrad
A Python toolbox for performing gradient-free optimization
Nevergrad is a Python library for derivative-free optimization, offering robust implementations of many algorithms suited for black-box functions (i.e. functions where gradients are unavailable or unreliable). It targets hyperparameter search, architecture search, control problems, and experimental tuning—domains in which gradient-based methods may fail or be inapplicable. The library provides an easy interface to define an optimization problem (parameter space, loss function, budget) and then experiment with multiple strategies—evolutionary algorithms, Bayesian optimization, bandit methods, genetic algorithms, etc. ... -
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Agno
Lightweight framework for building Agents with memory, knowledge, etc.
...Agno embraces multi-agent environments and symbolic reasoning as part of its core design, enabling experiments with structured knowledge, goal-oriented behaviors, and meta-learning. It’s designed for researchers seeking an extensible platform to explore AGI components without being tied to black-box models.
