Firmware Security Reports
OrbitCurve/firmware-reverse-engineering
Evidence-based security report generation for firmware assessments.
Analyze malicious Linux ELF binaries — botnets, cryptominers, ransomware, and rootkits targeting Linux servers, containers, and cloud infrastructure — through static analysis, dynamic tracing, and…
The automated check flagged lines worth reading first. See the safety section below.
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-linux-elf-malware -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills analyzing-linux-elf-malware --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/analyzing-linux-elf-malware .claude/skills/analyzing-linux-elf-malware && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "analyzing-linux-elf-malware" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-linux-elf-malware into .claude/skills/analyzing-linux-elf-malware/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyzing-linux-elf-malware", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-linux-elf-malwareType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-linux-elf-malware -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills analyzing-linux-elf-malware --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/analyzing-linux-elf-malware .agents/skills/analyzing-linux-elf-malware && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "analyzing-linux-elf-malware" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-linux-elf-malware into .agents/skills/analyzing-linux-elf-malware/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyzing-linux-elf-malware", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-linux-elf-malware -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills analyzing-linux-elf-malware --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/analyzing-linux-elf-malware .cursor/skills/analyzing-linux-elf-malware && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "analyzing-linux-elf-malware" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-linux-elf-malware into .cursor/skills/analyzing-linux-elf-malware/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyzing-linux-elf-malware", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git --path skills/analyzing-linux-elf-malware--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-linux-elf-malware -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills analyzing-linux-elf-malware --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/analyzing-linux-elf-malware .gemini/skills/analyzing-linux-elf-malware && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "analyzing-linux-elf-malware" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-linux-elf-malware into .gemini/skills/analyzing-linux-elf-malware/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyzing-linux-elf-malware", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills analyzing-linux-elf-malwareInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-linux-elf-malware -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/analyzing-linux-elf-malware .github/skills/analyzing-linux-elf-malware && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "analyzing-linux-elf-malware" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-linux-elf-malware into .github/skills/analyzing-linux-elf-malware/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyzing-linux-elf-malware", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-linux-elf-malware -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills analyzing-linux-elf-malware --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/analyzing-linux-elf-malware .opencode/skills/analyzing-linux-elf-malware && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "analyzing-linux-elf-malware" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-linux-elf-malware into .opencode/skills/analyzing-linux-elf-malware/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyzing-linux-elf-malware", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
analyzing-linux-elf-malwareAnalyze malicious Linux ELF binaries — botnets, cryptominers, ransomware, and rootkits targeting Linux servers, containers, and cloud infrastructure — through static analysis, dynamic tracing, and…
Analyzing Linux Elf Malware is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Analyze malicious Linux ELF binaries — botnets, cryptominers, ransomware, and rootkits targeting Linux servers, containers, and cloud infrastructure — through static analysis, dynamic tracing, and reverse engineering of x8664 and ARM samples. Use when investigating Linux malware, triaging a suspicious ELF binary, assessing a compromised Linux server, or analyzing container-targeted malware.
Its SKILL.md is about 3.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `references/api-reference.md` and `scripts/agent.py`).
It sits in Security, covering Linux administration, Reverse engineering and malware and Static analysis and SAST. It works with Linux and Ghidra. The repository describes itself as: 817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io…. The licence is Apache-2.0.
6 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 54a7988. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
shwgetcurlFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use wget and curl, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Analyzing Linux Elf Malware loads about 3.1k tokens when it runs, and up to ~3.9k if it reads all its reference files. Until then it costs about 106 tokens; SKILL.md has 618 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found patterns that need a careful read before installing.
grep -iE "(ssh|authorized_keys|id_rsa|shadow|passwd)" strings_output.txtAutomated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
The full file from mukul975/Anthropic-Cybersecurity-Skills at commit 54a7988, republished under its Apache-2.0 licence (© mukul975). 618 words, ~3,119 tokens.
.claude/skills/analyzing-linux-elf-malware/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.Do not use for Windows PE binary analysis; use PEStudio, Ghidra, or IDA for Windows malware.
Examine the ELF header and basic properties:
# File type identification
file suspect_binary
# Detailed ELF header analysis
readelf -h suspect_binary
# Section headers
readelf -S suspect_binary
# Program headers (segments)
readelf -l suspect_binary
# Symbol table (if not stripped)
readelf -s suspect_binary
nm suspect_binary 2>/dev/null
# Dynamic linking information
readelf -d suspect_binary
ldd suspect_binary 2>/dev/null # Only on matching architecture!
# Compute hashes
md5sum suspect_binary
sha256sum suspect_binary
# Check for packing/UPX
upx -t suspect_binary# Python-based ELF analysis
from elftools.elf.elffile import ELFFile
import hashlib
with open("suspect_binary", "rb") as f:
data = f.read()
sha256 = hashlib.sha256(data).hexdigest()
with open("suspect_binary", "rb") as f:
elf = ELFFile(f)
print(f"SHA-256: {sha256}")
print(f"Class: {elf.elfclass}-bit")
print(f"Endian: {elf.little_endian and 'Little' or 'Big'}")
print(f"Machine: {elf.header.e_machine}")
print(f"Type: {elf.header.e_type}")
print(f"Entry Point: 0x{elf.header.e_entry:X}")
# Check if stripped
symtab = elf.get_section_by_name('.symtab')
print(f"Stripped: {'Yes' if symtab is None else 'No'}")
# Section entropy analysis
import math
from collections import Counter
for section in elf.iter_sections():
data = section.data()
if len(data) > 0:
entropy = -sum((c/len(data)) * math.log2(c/len(data))
for c in Counter(data).values() if c > 0)
if entropy > 7.0:
print(f" [!] High entropy section: {section.name} ({entropy:.2f})")Search for embedded IOCs and functionality clues:
# ASCII strings
strings suspect_binary > strings_output.txt
# Search for network indicators
grep -iE "(http|https|ftp)://" strings_output.txt
grep -iE "([0-9]{1,3}\.){3}[0-9]{1,3}" strings_output.txt
grep -iE "[a-zA-Z0-9.-]+\.(com|net|org|io|ru|cn)" strings_output.txt
# Search for shell commands
grep -iE "(bash|sh|wget|curl|chmod|/tmp/|/dev/)" strings_output.txt
# Search for crypto mining indicators
grep -iE "(stratum|xmr|monero|pool\.|mining)" strings_output.txt
# Search for SSH/credential theft
grep -iE "(ssh|authorized_keys|id_rsa|shadow|passwd)" strings_output.txt
# Search for persistence mechanisms
grep -iE "(crontab|systemd|init\.d|rc\.local|ld\.so\.preload)" strings_output.txt
# FLOSS for obfuscated strings (if available)
floss suspect_binaryIdentify what system calls and libraries the malware uses:
# List imported functions (dynamically linked)
readelf -r suspect_binary | grep -E "socket|connect|exec|fork|open|write|bind|listen"
# Trace system calls during execution (in isolated VM only)
strace -f -e trace=network,process,file -o strace_output.txt ./suspect_binary
# Trace library calls
ltrace -f -o ltrace_output.txt ./suspect_binary
# Key system calls to watch:
# Network: socket, connect, bind, listen, accept, sendto, recvfrom
# Process: fork, execve, clone, kill, ptrace
# File: open, read, write, unlink, rename, chmod
# Persistence: inotify_add_watch (file monitoring)Debug the malware to observe runtime behavior:
# Start GDB with the binary
gdb ./suspect_binary
# Set breakpoints on key functions
(gdb) break main
(gdb) break socket
(gdb) break connect
(gdb) break execve
(gdb) break fork
# Run and analyze
(gdb) run
(gdb) info registers # View register state
(gdb) x/20s $rdi # Examine string argument
(gdb) bt # Backtrace
(gdb) continue
# For stripped binaries, break on entry point
(gdb) break *0x400580 # Entry point from readelf
(gdb) run
# Monitor network connections during execution
# In another terminal:
ss -tlnp # List listening sockets
ss -tnp # List established connectionsPerform deep code analysis on the ELF binary:
Ghidra Analysis for Linux ELF:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
1. Import: File -> Import -> Select ELF binary
- Ghidra auto-detects ELF format and architecture
- Accept default analysis options
2. Key analysis targets:
- main() function (or entry point if stripped)
- Socket creation and connection functions
- Command dispatch logic (switch/case on received data)
- Encryption/encoding routines
- Persistence installation code
- Self-propagation/scanning functions
3. For Mirai-like botnets, look for:
- Credential list for brute-forcing (telnet/SSH)
- Attack module selection (UDP flood, SYN flood, ACK flood)
- Scanner module (port scanning for vulnerable devices)
- Killer module (killing competing botnets)
4. For cryptominers, look for:
- Mining pool connection (stratum protocol)
- Wallet address strings
- CPU/GPU utilization functions
- Process hiding techniquesCheck for persistence mechanisms:
# Check for LD_PRELOAD rootkit
strings suspect_binary | grep "ld.so.preload"
# Malware writing to /etc/ld.so.preload can hook all dynamic library calls
# Check for crontab persistence
strings suspect_binary | grep -i "cron"
# Check for systemd service creation
strings suspect_binary | grep -iE "systemd|\.service|systemctl"
# Check for init script creation
strings suspect_binary | grep -iE "init\.d|rc\.local|update-rc"
# Check for SSH key injection
strings suspect_binary | grep -i "authorized_keys"
# Check for kernel module (rootkit) loading
strings suspect_binary | grep -iE "insmod|modprobe|init_module"
# Check for process hiding
strings suspect_binary | grep -iE "proc|readdir|getdents"| Term | Definition |
|---|---|
| ELF (Executable and Linkable Format) | Standard binary format for Linux executables, shared libraries, and core dumps containing headers, sections, and segments |
| Stripped Binary | ELF binary with debug symbols removed, making reverse engineering more difficult as function names are lost |
| LD_PRELOAD | Linux environment variable specifying shared libraries to load before all others; abused by rootkits to intercept system library calls |
| strace | Linux system call tracer that logs all system calls and signals made by a process, revealing file, network, and process operations |
| GOT/PLT | Global Offset Table and Procedure Linkage Table; ELF structures for dynamic linking that can be hijacked for function hooking |
| Statically Linked | Binary compiled with all library code included; common in IoT malware to run on systems without matching shared libraries |
| Mirai | Prolific Linux botnet targeting IoT devices via telnet brute-force; source code leaked, leading to many variants |
Context: A cloud server shows 100% CPU usage. Investigation reveals an unknown binary running from /tmp with a suspicious name. The binary needs analysis to confirm it is a cryptominer and identify the attacker's wallet and pool.
Approach:
file and readelf to identify architecture and linking typePitfalls:
ldd on malware outside a sandbox (ldd can execute code in the binary)LINUX ELF MALWARE ANALYSIS REPORT
====================================
File: /tmp/.X11-unix/.rsync
SHA-256: e3b0c44298fc1c149afbf4c8996fb924...
Type: ELF 64-bit LSB executable, x86-64
Linking: Statically linked (all libraries embedded)
Stripped: Yes
Size: 2,847,232 bytes
Packer: UPX 3.96 (unpacked for analysis)
CLASSIFICATION
Family: XMRig Cryptominer (modified)
Variant: Custom build with C2 update mechanism
FUNCTIONALITY
[*] XMR (Monero) mining via RandomX algorithm
[*] Stratum pool connection for work submission
[*] C2 check-in for configuration updates
[*] Process name masquerading (argv[0] = "[kworker/0:0]")
[*] Competitor process killing (kills other miners)
[*] SSH key injection for re-access
NETWORK INDICATORS
Mining Pool: stratum+tcp://pool.minexmr[.]com:4444
C2 Server: hxxp://update.malicious[.]com/config
Wallet: 49jZ5Q3b...Monero_Wallet_Address...
PERSISTENCE
[1] Crontab entry: */5 * * * * /tmp/.X11-unix/.rsync
[2] SSH key added to /root/.ssh/authorized_keys
[3] Systemd service: /etc/systemd/system/rsync-daemon.service
[4] Modified /etc/ld.so.preload for process hiding
PROCESS HIDING
LD_PRELOAD: /usr/lib/.libsystem.so
Hook: readdir() to hide /tmp/.X11-unix/.rsync from ls
Hook: fopen() to hide from /proc/*/maps reading© mukul975, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 3 other files (scripts, references) in skills/analyzing-linux-elf-malware of mukul975/Anthropic-Cybersecurity-Skills.
Open the folder on GitHubat commit 54a7988
Analyzing Linux Elf Malware next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Analyzing Linux Elf Malware this skillmukul975/Anthropic-Cybersecurity-Skills | 34k | — | ~3.1k | Automated safety check: Warn | Apache-2.0 | |
| Firmware Security ReportsOrbitCurve/firmware-reverse-engineering | 214 | — | ~4.1k | Automated safety check: Pass | Apache-2.0 | |
| Re Riscvdslsdzc/rev-skills | 125 | 1 repos | ~2.2k | Automated safety check: Pass | Apache-2.0 | |
| Binary Reaiskillstore/marketplace | 430 | 1 repos | ~2.6k | Automated safety check: Pass | None | |
| Re Armdslsdzc/rev-skills | 125 | — | ~2.2k | Automated safety check: Pass | Apache-2.0 | |
| Ghidra ReOrbitCurve/firmware-reverse-engineering | 214 | — | ~4.2k | Automated safety check: Pass | Apache-2.0 |
OrbitCurve/firmware-reverse-engineering
Evidence-based security report generation for firmware assessments.
dslsdzc/rev-skills
RISC-V 架构逆向:RV32/RV64、压缩指令(RVC)、gp 相对寻址、ABI 与 ecall 系统调用约定、工具链指纹。
aiskillstore/marketplace
This skill should be used when analyzing binaries, executables, or bytecode to understand what they do or how they work.
dslsdzc/rev-skills
ARM 架构逆向(非 Android):Cortex-M/A 向量表、Thumb/ARM 切换、AAPCS 调用约定、位置相关代码重定位、MMIO 外设寄存器交叉。
OrbitCurve/firmware-reverse-engineering
Expert-level Ghidra reverse engineering for firmware binaries with emphasis on stripped binary analysis, automated function discovery, cryptographic routine identification, authentication logic…
DavidClawson/OpenScope-2C53T
Run and record a hardware experiment on the 2C53T bench using a controlled five-step cycle.
mukul975/Anthropic-Cybersecurity-Skills
Weighs infrastructure, TTP, malware code and timing evidence with the Diamond Model and competing hypotheses to reach a confidence-rated attribution.
mukul975/Anthropic-Cybersecurity-Skills
Walks through reverse engineering Go-compiled malware in Ghidra: parsing buildinfo and pclntab, recovering stripped function names and extracting dependencies.
mukul975/Anthropic-Cybersecurity-Skills
Guides forensic analysis of Windows LNK shortcut files and Jump Lists with LECmd, JLECmd and manual parsing to show file access and program execution.
mukul975/Anthropic-Cybersecurity-Skills
Hunts Windows malware persistence with Sysinternals Autoruns, covering run keys, services, scheduled tasks and drivers, with baseline comparison.
mukul975/Anthropic-Cybersecurity-Skills
Guides a Windows forensic examination of the NTFS Master File Table to recover deleted-file evidence, build timelines and spot timestomping.
mukul975/Anthropic-Cybersecurity-Skills
Detects DNS tunneling, ICMP exfiltration and HTTP-based covert channels in packet captures and DNS logs when hunting for hidden command-and-control traffic.
Categories
Analyze malicious Linux ELF binaries — botnets, cryptominers, ransomware, and rootkits targeting Linux servers, containers, and cloud infrastructure — through static analysis, dynamic tracing, and…. Analyzing Linux Elf Malware is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Analyze malicious Linux ELF binaries — botnets, cryptominers, ransomware, and rootkits targeting Linux servers, containers, and cloud infrastructure — through static analysis, dynamic tracing, and reverse engineering of x8664 and ARM samples.
Analyzing Linux Elf Malware fits situations like: investigating Linux malware; triaging a suspicious ELF binary; assessing a compromised Linux server; analyzing container-targeted malware.
Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-linux-elf-malware -a claude-code`. Or copy the skill folder (skills/analyzing-linux-elf-malware in mukul975/Anthropic-Cybersecurity-Skills) into .claude/skills/analyzing-linux-elf-malware in your project. Claude Code loads it when a task matches its description.
Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-linux-elf-malware -a codex`. Or copy the skill folder (skills/analyzing-linux-elf-malware in mukul975/Anthropic-Cybersecurity-Skills) into .agents/skills/analyzing-linux-elf-malware in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-linux-elf-malware -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/analyzing-linux-elf-malware, .gemini/skills/analyzing-linux-elf-malware, .github/skills/analyzing-linux-elf-malware and .opencode/skills/analyzing-linux-elf-malware in your project.
Going by SKILL.md and its folder, Analyzing Linux Elf Malware needs Python for the scripts in its folder and the command-line tools its instructions call (sh, wget and curl). Our summary lists: Python 3; Docker.
SKILL.md contains no URLs. Its commands use wget and curl, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Our automated static check of SKILL.md flagged 1 warning(s): mentions a credentials file (ssh keys, cloud or package-manager tokens). Read the flagged lines before installing; the check is not a guarantee either way. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
Analyzing Linux Elf Malware is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
About 3.1k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 782 tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Analyzing Linux Elf Malware: Firmware Security Reports (OrbitCurve/firmware-reverse-engineering, 214 stars), Re Riscv (dslsdzc/rev-skills, 125 stars), Binary Re (aiskillstore/marketplace, 430 stars) and Re Arm (dslsdzc/rev-skills, 125 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
mukul975 (a GitHub user) maintains it in mukul975/Anthropic-Cybersecurity-Skills, which has 33,922 GitHub stars. The repository holds 637 skills in this directory. The repository was last updated on August 31, 2026.
Source: mukul975/Anthropic-Cybersecurity-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.