126 matches found
MeshCore
About MeshCore MeshCore is a lightweight, portable C++ library that enables multi-hop packet routing for embedded projects using LoRa and other packet radios. It is designed for developers who want to create resilient, decentralized communication networks that work without the internet. 🔍 What is...
MeshTNC
MeshTNC MeshTNC is a tool for piping LoRa data to and from consumer grade radios. Code - github.com/datapartyjs/MeshTNC Releases - github.com/datapartyjs/MeshTNC/releases Support - ko-fi/dataparty Features Simple serial cli built into the firmware Transmit raw bytes hex over LoRA using serial CLI...
DeeCLIP
DeeCLIP: A Robust and Generalizable Transformer-Based Framework for Detecting AI-Generated Images ☀️ If you find this work useful for your research, please kindly star our repo and cite our paper! ☀️ TODO We are working hard on the following items: Release ArXiv paper Release training scripts...
DeepGuard
DeepGuard 📖 Project Overview DeepGuard is an innovative secure code generation approach that enhances large language models' capability for secure code generation through multi-layer semantic aggregation techniques. This method effectively identifies and mitigates security vulnerabilities in code...
Reflections and Fragments: Securing LLMs against Sequential Mosaic Attacks
Self-play red-teaming improves language-model safety by pitting attacker and defender roles against each other in a zero-sum game. However, real adversaries increasingly use mosaic attacks: multi-turn sequences whose individual fragments are innocuous in isolation yet assemble into a harmful...
Backdoor Containment Via Expert Quarantine and Shutdown in LLMs
Backdoored large language models LLMs can behave normally on benign inputs while producing attacker-specified outputs under hidden triggers. Existing defenses span four stages--prior-training, in-training, post-training, and inference-time--and share one of two underlying strategies: either...
Backdoor Purification for LoRA-Tuned LLMs Via Null-Space Projection
With the rapid adoption of large language models LLMs and parameter-efficient fine-tuning PEFT methods, the risk of backdoor attacks has become more severe. Existing backdoor purification methods typically rely on at least one of the strong assumptions, such as prior knowledge of triggers, access...
Forensic-Aware Continual Adaptation for Image Forgery Localization
The rapid evolution of image manipulation techniques has raised growing public security concerns. Existing Image Forgery Localization IFL methods can accurately localize manipulated regions but are often unable to adapt to newly emerging forgeries. In real-world forensic scenarios, data typically...
CoDeL: Co-Evolutionary Defense against Indirect Prompt Injection in LLM-Based Agents
Large language model LLM-based agents increasingly rely on external tools and content, exposing them to indirect prompt injection IPI. This threat has motivated a wide range of defenses, among which training-based defenses are often regarded as most reliable. However, existing training-based...
Tokenized Key-Gated Adapter Routing: A Secure Access Control Mechanism against Private Data Leakage in LLMs
Large language models LLMs are increasingly deployed in privacy-critical domains e.g., healthcare, finance, and government, but their propensity to memorize and disclose personally identifiable information PII poses serious security and compliance risks. Existing defenses typically force a...
AGENTQ: Quantization-Conditioned Backdoor Attacks on LLM Agents
Quantization is one of the default deployment paths for open-weight LLM agents, but it is not behavior-preserving: an adversary can release a full-precision checkpoint that passes audits yet misbehaves once quantized, termed as quantization-conditioned attack QCA. Prior QCA work targets free-text...
A Three-Axis Stress Test of LLM Vs Classical ML for Network Intrusion Detection under Distribution Shift and Adversarial Evasion
Large language models are increasingly benchmarked against classical machine learning for network intrusion detection NIDS, almost always using same-dataset evaluation, and that protocol turns out to be incomplete. Evaluating XGBoost and RoBERTa-LoRA on two independently collected NetFlow v2...
HiveTraceGuard-Pro: A Compact Generative Guardrail for Prompt Injection, Jailbreaks, and Adversarial Obfuscation
Production LLMs must handle inputs that attempt to override system instructions, bypass safety policies or elicit harmful responses. A common mitigation is a separate guardrail model. Existing reports, however, provide little evidence on Russian prompt injection or Russian surface obfuscation. We...
qwen3.8-9b-cyber-exploit-agent-uncensored
Qwen3.8-9B Cyber Exploit Agent — UNCENSORED Identical fine-tu...
MeshCore room-server-v1.17.0
About MeshCore MeshCore is a lightweight, portable C++ library that enables multi-hop packet routing for embedded projects using LoRa and other packet radios. It is designed for developers who want to create resilient, decentralized communication networks that work without the internet. 🔍 What is...
CVE-2026-71281
Hugging Face peft's LoRA-GA and CorDA initialization modules src/peft/tuners/lora/corda.py lines 102 and 163, and src/peft/tuners/lora/loraga.py line 101 call torch.load on config-specified cache/covariance files without weightsonly=True, bypassing peft's own safe-loading wrapper used elsewhere i...
CVE-2026-71281
The CVE-2026-71281 details a vulnerability in Hugging Face PEFT: LoRA-GA and CorDA initialization modules call torch.load() on cache/covariance files without weights_only=True, bypassing PEFT’s safe-loading wrapper. This full pickle deserialization can lead to arbitrary code execution if a malici...
CVE-2026-71281
Hugging Face peft's LoRA-GA and CorDA initialization modules src/peft/tuners/lora/corda.py lines 102 and 163, and src/peft/tuners/lora/loraga.py line 101 call torch.load on config-specified cache/covariance files without weightsonly=True, bypassing peft's own safe-loading wrapper used elsewhere i...
CVE-2026-71281 peft Unsafe Deserialization via torch.load() Without weights_only in LoRA-GA and CorDA Modules
Hugging Face peft's LoRA-GA and CorDA initialization modules src/peft/tuners/lora/corda.py lines 102 and 163, and src/peft/tuners/lora/loraga.py line 101 call torch.load on config-specified cache/covariance files without weightsonly=True, bypassing peft's own safe-loading wrapper used elsewhere i...
EUVD-2026-53366
Hugging Face peft's LoRA-GA and CorDA initialization modules src/peft/tuners/lora/corda.py lines 102 and 163, and src/peft/tuners/lora/loraga.py line 101 call torch.load on config-specified cache/covariance files without weightsonly=True, bypassing peft's own safe-loading wrapper used elsewhere i...