gemma_crackme_tutorial

Google의 Gemma 4 E4B 로컬 AI 모델을 사용하여 간단한 Crackme 리버스 엔지니어링하기 저는 Google이 출시한 새로운 Gemma E4B 오픈 가중치 로컬 모델을 가지고 놀던 중, 로컬 오프라인 리버스 엔지니어링 시나리오에서 놀라운 성공을 거두는 것을 보았습니다. 이 튜토리얼을 작성하여 로컬 AI가 이제 많은 기본적인 리버싱 작업에 충분히 좋다는 사실을 알리고, 앞으로 상황이 빠르게 개선될 것이라는 점을 전파하고자 합니다. 리버스 엔지니어링과 AI 새로운 바이너리를 리버싱할 때 가장 지루한 부분 중 하나는...

PT-2026-67349

Name of the Vulnerable Software and Affected Versions huggingface/transformers versions prior to 5.8.0.dev0 Description An issue allows arbitrary file writes through path traversal, a technique used to access files and directories outside the intended folder. The flaw exists in the save pretraine...

Beyond Refusal: A Same-Lineage Study of Aligned and Abliterated LLMs for Vulnerability Analysis

Large language model LLM-assisted software security operates at a difficult boundary: the vulnerability-analysis terminology needed for legitimate code review, triage, and repair can closely resemble terminology associated with misuse. Existing safety and cybersecurity evaluations are difficult t...

Intelligent Detection and Mitigation of Carpet-Bombing DDoS Attacks in SDN Using Retrieval-Augmented Generation and Large Language Models

Software-Defined Networking SDN provides flexible and programmable network management; however, its centralized control architecture remains highly vulnerable to Distributed Denial-of-Service DDoS attacks, particularly Carpet-Bombing DDoS attacks that distribute malicious traffic across multiple...

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...

Analysis of LLMs against Prompt Injection and Jailbreak Attacks

Large Language Models LLMs are widely deployed in real-world systems. Given their broader applicability, prompt engineering has become an efficient tool for resource-scarce organizations to adopt LLMs for their own purposes. At the same time, LLMs are vulnerable to prompt-based attacks. Thus,...

Emoji-Based Jailbreaking of Large Language Models

Large Language Models LLMs are integral to modern AI applications, but their safety alignment mechanisms can be bypassed through adversarial prompt engineering. This study investigates emoji-based jailbreaking, where emoji sequences are embedded in textual prompts to trigger harmful and unethical...

REx86: A Local Large Language Model for Assisting in X86 Assembly Reverse Engineering

Reverse engineering RE of x86 binaries is indispensable for malware and firmware analysis, but remains slow due to stripped metadata and adversarial obfuscation. Large Language Models LLMs offer potential for improving RE efficiency through automated comprehension and commenting, but cloud-hosted...

Invariant-Based Robust Weights Watermark for Large Language Models

Watermarking technology has gained significant attention due to the increasing importance of intellectual property IP rights, particularly with the growing deployment of large language models LLMs on billions resource-constrained edge devices. To counter the potential threats of IP theft by...