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 skills add 26zl/cybersec-toolkit --skill deobfuscating-javascript-malwaregit clone --depth 1 https://github.com/26zl/cybersec-toolkitWrote 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/26zl/cybersec-toolkit/deobfuscating-javascript-malware)<a href="https://agentmods.dev/skills/26zl/cybersec-toolkit/deobfuscating-javascript-malware"><img src="https://agentmods.dev/badge/skills/26zl/cybersec-toolkit/deobfuscating-javascript-malware/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.
<a href="https://agentmods.dev/skills/26zl/cybersec-toolkit/deobfuscating-javascript-malware"><img src="https://agentmods.dev/badge/skills/26zl/cybersec-toolkit/deobfuscating-javascript-malware.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00082 | $0.03133 |
| Opus 5.5 | $0.00033 | $0.01253 |
| Sonnet 5.5 | $0.00016 | $0.00627 |
| Haiku 4.5 | $0.00008 | $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 1mo ago.
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 Copies of this mod
2 near-identical copies found in the catalogue:
- deobfuscating-javascript-malware — 100% identical, 0 lines differ
- deobfuscating-javascript-malware — 89% identical, 8 lines differ
How it starts
The opening of the file, as written. The whole thing — 367 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
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.
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.
- 1mo ago First seen · 367 lines · 82 tokens per session scan B c398220aa659
deobfuscating-javascript-malware is a skill published in the GitHub repository 26zl/cybersec-toolkit (82 stars, last pushed yesterday), licensed MIT. It adds 82 tokens to every session and 3,133 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it B with 2 findings (encoded or obfuscated payload, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
deobfuscating-javascript-malware
Deobfuscates malicious JavaScript found in phishing pages, web skimmers, and dropper scripts by reversing encoding layers, eval chains, string manipulation, and control-flow obfuscation to reveal the original malicious logic. Use when investigating a phishing page's obfuscated JavaScript, analyzing a Magecart-style…
deobfuscating-javascript-malware
Deobfuscates malicious JavaScript code used in web-based attacks, phishing pages, and dropper scripts by reversing encoding layers, eval chains, string manipulation, and control flow obfuscation to reveal the original malicious logic. Activates for requests involving JavaScript malware analysis, script deobfuscation…
argus
Argus — the all-seeing scanner suite. Six automated scanners for high-value web + LLM bug classes — CORS misconfiguration (origin reflection / null / credentialed read), CRLF & host-header injection, NoSQL injection (operator auth-bypass / $where blind), JWT attacks (alg:none / RS256→HS256 confusion / secret crack)…
exploiting-format-string-vulnerabilities
Methodology for exploiting format string bugs where attacker-controlled data reaches the format argument of printf-family functions, enabling stack/memory disclosure (info leaks for ASLR/PIE/canary defeat) and arbitrary write primitives (%n) to hijack control flow via GOT/.finiarray overwrites.
exploiting-integer-overflow-vulnerabilities
Methodology for finding and exploiting integer overflow, underflow, truncation, and signedness bugs in native code during authorized engagements, focusing on how wrapped arithmetic in size/length calculations leads to undersized allocations, oversized copies, length-check bypasses, and downstream heap/stack overflows.
exploiting-linux-kernel-vulnerabilities
Methodology for discovering and exploiting Linux kernel memory-corruption vulnerabilities (UAF, OOB read/write, race/TOCTOU, type confusion) during authorized engagements, covering reachability analysis, building stable read/write primitives from a single bug, defeating KASLR/SMEP/SMAP/KPTI, slab/buddy heap grooming…