Exponentiated-Gradient-Descent-LLM-Attack
sbamit/exponentiated-gradient-descent-llm-attackA novel adversarial attack on LLM based on the Exponentiated Gradient Descent technique.
adversarial-attackai-securitymachine-learning+1
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sbamit/exponentiated-gradient-descent-llm-attackA novel adversarial attack on LLM based on the Exponentiated Gradient Descent technique.
paul12345-cyber/multimodal-web-attack-datasetThis repository hosts a multimodal web attack dataset (MWAD) to advance AI-driven threat detection research.
bishopfox/brokenhillA productionized greedy coordinate gradient (GCG) attack tool for large language models (LLMs)
xueboqiu/prohunterGraph-based threat detection system using inexact graph vector matching to compare threat graphs with CTI-derived attack query graphs for automated…
microsoft/pyritOpen-source framework for red-teaming generative AI systems: automate attack prompts, score model responses, and audit behavior to identify security…
toxy4ny/delirium-ai-safety-benchmarkA diagnostic framework for measuring LLM vulnerability to Affective Contextual Erosion (ACE) and related liminal attack vectors. **Delirium** is not…
phkhanhtrinh23/agasLLM-driven agentic group shilling attack framework that manipulates black-box collaborative-filtering recommender rankings using adaptive multi-role…
protectai/rebuffMulti-layered prompt injection detector for AI applications using heuristics, LLM-based analysis, vectorDB attack signatures, and canary token leak…
skac44/cve-2024-38063Simulates CVE-2024-38063 TCP/IP remote code execution attack, captures network traffic with TShark, and trains a machine learning model to detect…
trusted-ai/adversarial-robustness-toolboxPython library for adversarial machine learning security, enabling red and blue teams to run evasion, poisoning, extraction, and inference attacks…
trusted-ai/aif360A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in…
bethgelab/foolboxA Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX
safety-research/bloomAutomated behavioral evaluation framework for LLMs that generates diverse test scenarios to probe for sycophancy, bias, and other safety-relevant…
nccgroup/ml-for-rngsDemonstrates using machine learning to predict random number generator sequences, highlighting cryptographic weaknesses through adversarial analysis.
telefonica/omlaspFramework for auditing machine learning algorithms against adversarial attacks and biases, providing educational tools and an academic paper to…
pwnhyo/t-mapTrajectory-aware evolutionary search framework for red-teaming LLM agents over MCP servers, generating adversarial prompts to map vulnerability…
sec5gloc/sec5glocAdversary-resilient deep learning architecture for secure 5G indoor localization, combining CNN and multi-head attention to defend against signal…
atsukisato/pgm-attackResearch code for poisoning attacks on the PGM-index, demonstrating how to craft adversarial data to degrade learned index performance.