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.
npx agentmods add skills/youngmaidainon/agent-level-up/deobfuscating-javascript-malwarenpx skills add Youngmaidainon/Agent-Level-Up --skill deobfuscating-javascript-malwaregit clone --depth 1 https://github.com/Youngmaidainon/Agent-Level-UpWrote 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.
[](https://agentmods.dev/skills/youngmaidainon/agent-level-up/deobfuscating-javascript-malware)<a href="https://agentmods.dev/skills/youngmaidainon/agent-level-up/deobfuscating-javascript-malware"><img src="https://agentmods.dev/badge/skills/youngmaidainon/agent-level-up/deobfuscating-javascript-malware.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00091 | $0.03132 |
| Opus 5 | $0.00046 | $0.01566 |
| Sonnet 5 | $0.00018 | $0.00626 |
| Haiku 4.5 | $0.00009 | $0.00313 |
Grade B, and why
deobfuscating-javascript-malware scanned grade B with 2 findings 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 yesterday.
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.
Encoded or obfuscated payloadmediumSupply chain
base64 or hex that is decoded and executed hides what actually runs from anyone reading the file.
- eval(atob("...")) Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
import urllib.parse This is a copy
89% identical to deobfuscating-javascript-malware — 8 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 361 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deobfuscating JavaScript Malware
When to Use
- Investigating a phishing page with obfuscated JavaScript that performs credential harvesting or redirect
- Analyzing a web skimmer (Magecart-style) injected into an e-commerce site
- Deobfuscating a JavaScript dropper that downloads and executes second-stage malware
- Examining malicious email attachments containing HTML files with embedded obfuscated scripts
- Analyzing browser exploit kits that use heavy JavaScript obfuscation to hide exploit delivery
Do not use for obfuscated JavaScript that is merely minified production code; use a standard beautifier instead.
Prerequisites
- Node.js 18+ installed for executing and debugging JavaScript in a controlled environment
- Python 3.8+ with
jsbeautifierlibrary for code formatting - Browser developer tools (Chrome DevTools) for controlled execution in an isolated browser
- CyberChef (https://gchq.github.io/CyberChef/) for encoding/decoding operations
- de4js or JStillery for automated JavaScript deobfuscation
- Isolated analysis VM with no access to production systems or sensitive data
Workflow
Step 1: Safely Extract and Examine the Obfuscated Script
Isolate the malicious JavaScript without executing it:
# Extract JavaScript from HTML file
python3 << 'PYEOF'
from html.parser import HTMLParser
class ScriptExtractor(HTMLParser):
def __init__(self):
super().__init__()
self.in_script = False
self.scripts = []
self.current = ""
def handle_starttag(self, tag, attrs):
if tag == "script":
self.in_script = True
self.current = ""
def handle_endtag(self, tag):
if tag == "script":
self.in_script = False
if self.current.strip():
self.scripts.append(self.current)
def handle_data(self, data):
if self.in_script:
self.current += data
with open("malicious_page.html") as f:
parser = ScriptExtractor()
parser.feed(f.read())
for i, script in enumerate(parser.scripts):
with open(f"script_{i}.js", "w") as f:
f.write(script)
print(f"Extracted script_{i}.js ({len(script)} bytes)")
PYEOF
# Beautify the extracted JavaScript
npx js-beautify script_0.js -o script_0_pretty.js
What ships with it
2 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.
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.
- yesterday First seen · 361 lines · 91 tokens per session scan B 099c1c671f0f
deobfuscating-javascript-malware is a skill published in the GitHub repository Youngmaidainon/Agent-Level-Up (3 stars, last pushed 10d ago), licensed MIT. It adds 91 tokens to every session and 3,132 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it B with 2 findings (encoded or obfuscated payload, makes network calls). It is 89% identical to deobfuscating-javascript-malware, differing in 8 lines, and is treated as a copy.
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