promptmap

O O o.-. Humans, Do Not Resist! |/ ,-'-. / /, / /|.-.| / --O-- .--""" /\ /\ | o.o / Utku Sen's /|\ -'--' / /| | - | / | | | \ /| | | ' \ '/ \ ' | ' \ | ' / | ' \ | / | | | ./| /||| ./|||,| .// / / |-----| || || || || promptmap2는 맞춤형 LLM 애플리케이션을 위한 자동화된 프롬프트 인젝션 스캐너입니다. 두 가지 테스트 모드를 지원합니다...

CVE-2022-44877-white-box

문서 : https://docs.google.com/document/d/1rQ7e9i2AFzHbASfRu3nkgjIswbrS2AB2i7V9OK3dJLs/edit?usp=sharing Libprochider : https://github.com/gianlucaborello/libprocesshider /var/www/html YouTube 데모 : https://www.youtube.com/watch?v=V0HgzE8IElA&t=343s 테스트한 머신 박스 CentOS 7 - CentOS Web Panel 9.8.1146 Kal...

CVE-2021-44228-white-box

논문 저널: https://www.researchgate.net/publication/373214720PengujianKerentananpadaCVE-2021-44228terhadapAncamanRemoteAccessTrojan 논문: https://1drv.ms/b/s!Al-8jtgY0iBXmz79DKEROaZT5n8a?e=1E6Pkm Log4Shell // n. Log4j 취약점 환경 박스 이 저장소는 CVE-2021-44228에 기반한 Log4j 취약점 테스트 환경을 위해 의도적으로 구축되었습니다. 이 환경은 GUI 데스...

Cobra

Cobra 이 프로젝트 설계는 현재 화이트박스 스캔 요구 사항을 충족할 수 없으며, 더 이상 유지보수되지 않습니다. 연구 목적으로만 사용하고 프로덕션 환경에서는 사용하지 마십시오. 소개Introduction Cobra는 소스 코드 보안 감사 도구로, 여러 개발 언어 소스 코드에서 대부분의 주요 보안 문제와 취약점을 탐지할 수 있습니다. 특징Features GUI/CLI/API 모드명령줄 모드 및 API 모드 로컬 웹 서버 서비스를 제공하여 GUI 시각적 조작이 가능하며, 로컬 API 인터페이스도 지원하여 다른 시스템배포 시스템...

gradient-untangler

gradient-untangler A local research harness that searches for the exact tokens that make an open-weight language model start its answer the way you specify. Open-weight models ship as ordinary files: config.json, a tokenizer, and one or more .safetensors or .bin shards. A Hugging Face id is only ...

DroidBreaker: Practical and Functional Problem-Space Attacks on Machine-Learning Android Malware Detectors

Adversarial APKs are Android applications modified in the problem space to evade machine-learning malware detectors. In this work, we first show that, despite claims, existing problem-space attacks remain largely impractical. Most techniques leverage software transplantation to inject entire beni...

Assessing Automated Prompt Injection Attacks in Agentic Environments

Indirect prompt injection poses a critical threat to LLM agents that interact with untrusted external data, yet automated attack methods--proven effective for jailbreaking--remain underexplored in realistic agentic settings. We present a comprehensive empirical evaluation of automated prompt...

MRMMIA: Membership Inference Attacks on Memory in Chat Agents

Membership inference attacks MIAs test whether a target data record belongs to a system's private data, and have become a standard tool to measure privacy leakage in machine learning systems. Prior work has primarily focused on training corpora or retrieval databases. However, MIAs against agent...

Re-Triggering Safeguards within LLMs for Jailbreak Detection

This paper proposes a jailbreaking prompt detection method for large language models LLMs to defend against jailbreak attacks. Although recent LLMs are equipped with built-in safeguards, it remains possible to craft jailbreaking prompts that bypass them. We argue that such jailbreaking prompts ar...

Attention Is Where You Attack

Safety-aligned large language models rely on RLHF and instruction tuning to refuse harmful requests, yet the internal mechanisms implementing safety behavior remain poorly understood. We introduce the Attention Redistribution Attack ARA, a white-box adversarial attack that identifies...

Towards Optimal Agentic Architectures for Offensive Security Tasks

Agentic security systems increasingly audit live targets with tool-using LLMs, but prior systems fix a single coordination topology, leaving unclear when additional agents help and when they only add cost. We treat topology choice as an empirical systems question. We introduce a controlled...

Can Drift-Adaptive Malware Detectors Be Made Robust? Attacks and Defenses under White-Box and Black-Box Threats

Concept drift and adversarial evasion are two major challenges for deploying machine learning-based malware detectors. While both have been studied separately, their combination, the adversarial robustness of drift-adaptive detectors, remains unexplored. We address this problem with AdvDA, a rece...

Towards Unveiling Vulnerabilities of Large Reasoning Models in Machine Unlearning

Large language models LLMs possess strong semantic understanding, driving significant progress in data mining applications. This is further enhanced by large reasoning models LRMs, which provide explicit multi-step reasoning traces. On the other hand, the growing need for the right to be forgotte...

The Role of Learning in Attacking Intrusion Detection Systems

Recent work on network attacks have demonstrated that ML-based network intrusion detection systems NIDS can be evaded with adversarial perturbations. However, these attacks rely on complex optimizations that have large computational overheads, making them impractical in many real-world settings. ...

AEGIS: White-Box Attack Path Generation Using LLMs and Training Effectiveness Evaluation for Large-Scale Cyber Defence Exercises

Creating attack paths for cyber defence exercises requires substantial expert effort. Existing automation requires vulnerability graphs or exploit sets curated in advance, limiting where it can be applied. We present AEGIS, a system that generates attack paths using LLMs, white-box access, and...

Rectifying Adversarial Examples Using Their Vulnerabilities

Deep neural network-based classifiers are prone to errors when processing adversarial examples AEs. AEs are minimally perturbed input data undetectable to humans posing significant risks to security-dependent applications. Hence, extensive research has been undertaken to develop defense mechanism...

WuppieFuzz: Coverage-Guided, Stateful REST API Fuzzing

Many business processes currently depend on web services, often using REST APIs for communication. REST APIs expose web service functionality through endpoints, allowing easy client interaction over the Internet. To reduce the security risk resulting from exposed endpoints, thorough testing is...

One Leak Away: How Pretrained Model Exposure Amplifies Jailbreak Risks in Finetuned LLMs

Finetuning pretrained large language models LLMs has become the standard paradigm for developing downstream applications. However, its security implications remain unclear, particularly regarding whether finetuned LLMs inherit jailbreak vulnerabilities from their pretrained sources. We investigat...

Physical ID-Transfer Attacks against Multi-Object Tracking Via Adversarial Trajectory

Multi-Object Tracking MOT is a critical task in computer vision, with applications ranging from surveillance systems to autonomous driving. However, threats to MOT algorithms have yet been widely studied. In particular, incorrect association between the tracked objects and their assigned IDs can...

QueryIPI: Query-Agnostic Indirect Prompt Injection on Coding Agents

Modern coding agents integrated into IDEs combine powerful tools and system-level actions, exposing a high-stakes attack surface. Existing Indirect Prompt Injection IPI studies focus mainly on query-specific behaviors, leading to unstable attacks with lower success rates. We identify a more sever...