ctf-ai-ml은(는) 무엇을 하나요?
CTF 챌린지를 위한 AI 및 머신러닝 기술을 제공합니다. ML 모델 공격, 적대적 예시 생성, 모델 추출, 프롬프트 인젝션, 멤버십 추론, 훈련 데이터 중독, 미세 조정 조작, 신경망 분석, LoRA 어댑터 익스플로잇, LLM 탈옥 또는 AI 관련 퍼즐 해결 시 사용하세요.
CTF 챌린지를 위한 AI 및 머신러닝 기술을 제공합니다. ML 모델 공격, 적대적 예시 생성, 모델 추출, 프롬프트 인젝션, 멤버십 추론, 훈련 데이터 중독, 미세 조정 조작, 신경망 분석, LoRA 어댑터 익스플로잇, LLM 탈옥 또는 AI 관련 퍼즐 해결 시 사용하세요.
이 스킬은 CTF(Capture The Flag) 챌린지에서 AI 및 머신러닝 모델을 공격하고 분석하는 데 필요한 고급 기술을 제공합니다. 사용자는 적대적 예시(adversarial examples) 생성, 모델 추출(model extraction), 프롬프트 인젝션(prompt injection), 멤버십 추론(membership inference), 훈련 데이터 중독(training data poisoning), 미세 조정 조작(fine-tuning manipulation) 등 다양한 공격 벡터를 수행할 수 있습니다. 또한 신경망 구조 분석, LoRA 어댑터 익스플로잇, LLM 탈옥(jailbreaking)과 같은 최신 AI 보안 주제를 다룹니다. Python, TensorFlow, PyTorch 등 주요 ML 프레임워크를 지원하며, 명령줄 도구와 스크립트를 통해 실제 CTF 환경에서 즉시 활용 가능합니다. 이 스킬은 AI 모델의 취약점을 이해하고 이를 악용하는 방법을 학습하려는 보안 연구자와 CTF 참가자에게 필수적입니다.
npx skills add https://github.com/ljagiello/ctf-skills/tree/main/skills/ctf-ai-mlInstalled? Explore more 연구 & 데이터 분석 skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
CTF 챌린지를 위한 AI 및 머신러닝 기술을 제공합니다. ML 모델 공격, 적대적 예시 생성, 모델 추출, 프롬프트 인젝션, 멤버십 추론, 훈련 데이터 중독, 미세 조정 조작, 신경망 분석, LoRA 어댑터 익스플로잇, LLM 탈옥 또는 AI 관련 퍼즐 해결 시 사용하세요.
This repo contains 10 individual skills — each has its own dedicated page.
Provides cryptography attack techniques for CTF challenges. Use when attacking encryption, hashing, signatures, ZKP, PRNG, or mathematical crypto problems involving RSA, AES, ECC, lattices, LWE, CVP, number theory, Coppersmith, Pollard, Wiener, padding oracle, GCM, key derivation, or stream/block cipher weaknesses.
Provides digital forensics and signal analysis techniques for CTF challenges. Use when analyzing disk images, memory dumps, event logs, network captures, cryptocurrency transactions, steganography, PDF analysis, Windows registry, Volatility, PCAP, Docker images, coredumps, side-channel power traces, DTMF audio spectrograms, packet timing analysis, CD audio disc images, or recovering deleted files and credentials.
Provides malware analysis and network traffic techniques for CTF challenges. Use when analyzing obfuscated scripts, malicious packages, custom crypto protocols, C2 traffic, PE/.NET binaries, RC4/AES encrypted communications, YARA rules, shellcode analysis, memory forensics for malware (Volatility malfind, process injection detection), anti-analysis techniques (VM/sandbox detection, timing evasion, API hashing, process injection, environment checks), or extracting malware configurations and indicators of compromise.
Provides miscellaneous CTF challenge techniques for problems that do not cleanly fit the main categories. Use for encoding puzzles, pyjails, bash jails, RF/SDR, DNS oddities, unicode tricks, esoteric languages, QR or audio puzzles, constraint solving, game theory, unusual sandbox escapes, and hybrid logic puzzles. Prefer a more specific skill first when the challenge is mainly web, pwn, reverse, forensics, malware, OSINT, or crypto. Treat this as the fallback skill for genuine cross-category or edge-case challenges, not the default starting point.
Provides open source intelligence techniques for CTF challenges. Use when gathering information from public sources, social media, geolocation, DNS records, username enumeration, reverse image search, Google dorking, Wayback Machine, Tor relays, FEC filings, or identifying unknown data like hashes and coordinates.
Provides binary exploitation techniques for CTF challenges. Use when you already have a vulnerable native target or service and need to turn memory corruption or low-level primitives into code execution or privilege escalation, such as buffer overflows, format strings, heap bugs, ROP, ret2libc, shellcode, kernel exploitation, seccomp bypass, sandbox escape, or Windows/Linux exploit chains. Do not use it when the main blocker is understanding what the binary does; use reverse engineering first. Do not use it for pure web bugs, disk or packet forensics, or standalone crypto/math challenges.
Provides reverse engineering techniques for CTF challenges. Use when the main job is to understand how a compiled, obfuscated, packed, or virtualized target works before exploiting or solving it, including binaries, APKs, WASM, firmware, custom VMs, bytecode, game clients, malware-like loaders, and anti-debug or anti-analysis logic. Do not use it when the vulnerability is already understood and the remaining task is exploitation; use pwn instead. Do not use it for pure web workflows, log or disk forensics, or standalone crypto problems unless reversing the implementation is the real blocker.
Provides web exploitation techniques for CTF challenges. Use when the target is primarily an HTTP application, API, browser client, template engine, identity flow, or smart-contract frontend/backend surface, including XSS, SQLi, SSTI, SSRF, XXE, JWT, auth bypass, file upload, request smuggling, OAuth/OIDC, SAML, prototype pollution, and similar web bugs. Do not use it for native binary memory corruption, reverse engineering of standalone executables, disk or memory forensics, or pure cryptanalysis unless the web flaw is still the main path to the flag.
Generates a single standardized submission-style CTF writeup for competition handoff and organizer review. Use after solving a CTF challenge to document the solution steps, tools used, and lessons learned in a structured format.
Solves CTF challenges by performing first-pass triage, identifying the dominant category, and routing execution to the right specialized ctf-* skill. Use when the user gives you a challenge bundle, a remote service, a suspicious file, or only a vague challenge description and you must determine where to start. Do not use it when the category is already clear and a specialized skill can be invoked directly; this is the dispatcher and recon entrypoint, not the deepest reference for category-specific techniques.
USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.
Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.
Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
Agent skill repository discovered by 10x-chat research.
Review backend code for quality, security, maintainability, and best practices based on established checklist rules. Use when the user requests a review, analysis, or improvement of backend files (e.g., `.py`) under the `api/` directory. Do NOT use for frontend files (e.g., `.tsx`, `.ts`, `.js`). Supports pending-change review, code snippets review, and file-focused review.