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 meltedinhex/analyst-ai-pack --skill defeating-string-and-api-obfuscationgit clone --depth 1 https://github.com/meltedinhex/analyst-ai-packWrote 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/meltedinhex/analyst-ai-pack/defeating-string-and-api-obfuscation)<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/defeating-string-and-api-obfuscation"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/defeating-string-and-api-obfuscation/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/meltedinhex/analyst-ai-pack/defeating-string-and-api-obfuscation"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/defeating-string-and-api-obfuscation.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 | $0.00066 | $0.00816 |
| Opus 5 | $0.00033 | $0.00408 |
| Sonnet 5 | $0.00013 | $0.00163 |
| Haiku 4.5 | $0.00007 | $0.00082 |
Grade A, and why
defeating-string-and-api-obfuscation scanned grade A with 0 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 9d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Defeating String and API Obfuscation
When to Use
- A binary has few readable strings because they are XOR/stack-encoded or built at runtime.
- The import table is sparse because APIs are resolved by hash at runtime.
- You need to recover C2 URLs, paths, and the real API set to understand behavior.
Do not use plain strings and conclude "no indicators" — modern malware hides strings;
absence of readable strings is itself a sign of obfuscation.
Prerequisites
- A disassembler/decompiler to read the decode routine, plus the bundled XOR/hash tooling.
- Optionally FLOSS for automated stack/decoded-string recovery.
Workflow
Step 1: Recognize the obfuscation type
Stack strings : bytes mov'd to the stack one/few at a time, then used
Single-byte XOR: a loop XORing a buffer with a constant
Multi-byte/RC4 : a keyed stream over a blob
API hashing : a hash compared against export-name hashes to resolve functions
Step 2: Recover XOR-encoded strings
If you find the key and ciphertext, decode directly. The script brute-forces single-byte XOR and surfaces readable results:
python scripts/analyst.py xor-strings sample.bin
Step 3: Reconstruct stack strings
Read the decompiler to collect the byte sequence assembled on the stack and reassemble it. For volume, FLOSS emulates and extracts these automatically.
Step 4: Resolve API hashing
Identify the hash algorithm (often ROR13/ROR7 additive, or djb2). Precompute hashes for known export names and match the constants in the binary back to functions:
python scripts/analyst.py api-hash --algo ror13 --hash 0x726774C
Step 5: Reannotate and extract IOCs
Apply recovered strings and API names back in the disassembler and extract the now-visible URLs, paths, and behavior.
Validation
- Decoded strings are meaningful (URLs, DLL/API names, paths), not random bytes.
- Resolved API names match the calls' usage in the surrounding code.
- The recovered API set explains behavior seen dynamically.
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.
- 9d ago First seen · 103 lines · 66 tokens per session scan A d0ed6dad13db
defeating-string-and-api-obfuscation is a skill published in the GitHub repository meltedinhex/analyst-ai-pack (22 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 66 tokens to every session and 816 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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