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CyberSecurityUPandClaude Opus 5
4b71ac63a0
New `mobile` engagement mode: `neurosploit mobile <app.apk|app.ipa|binary>` reverse-engineers a local artifact with a dedicated `mobile` agent set, all headless and provisioned on demand (Ghidra analyzeHeadless, MobSF REST/Docker, Frida, apktool/jadx, radare2). Twelve original, generic skills (agents_md/mobile/, English): static binary triage, APK static analysis, IPA static analysis, RASP & anti-tamper mapping, root/jailbreak detection + bypass, TLS pinning detection + bypass, anti-debug detection + bypass, obfuscation analysis & deobfuscation, code-integrity / tamper-check bypass, hardcoded-secrets extraction, insecure local storage, and mobile network traffic analysis. Findings are proven from the artifact (decompilation or Frida trace), non-destructively. - agents.rs: new `mobile` Library category (loaded, counted). - pipeline.rs: run_mobile() mirroring the host pipeline with a mobile recon and headless tooling doctrine; exported from the crate. - CLI: `Cmd::Mobile` + `Mode::Mobile`, wired in main and the TUI. - README + TUTORIAL document the new test type; engagement-modes badge + table updated; "New in v4.2.0" note. Version bumped to 4.2.0 across the workspace. 383 tests. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
19 lines
1.9 KiB
Markdown
19 lines
1.9 KiB
Markdown
# Obfuscation Analysis and Deobfuscation
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## User Prompt
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You are analysing **{target}** (a binary, APK or IPA on disk) for: Obfuscation Analysis and Deobfuscation. CWE-656
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**Context:**
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{recon_json}
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All tools run HEADLESS (no GUI). Provision what you need on demand (apt/pip/go); time-box each install and skip on failure. Only test artifacts you are authorized to test.
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### Method
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1. Classify the obfuscation: identifier renaming (ProGuard/R8 mapping loss), string encryption, control-flow flattening, API-hashing/dynamic dispatch, packing/virtualization, native-code lifting.
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2. String decrypt: locate the decryptor routine (a function returning strings, called with constants), then either hook it with Frida to log plaintext at runtime, or reimplement it and batch-decrypt statically.
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3. Control-flow: use decompiler simplification (Ghidra P-code / r2 `agf`) to recover the real graph; for API-hashing, resolve the hashes against a symbol dictionary.
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4. Report the obfuscation techniques present, whether they meaningfully impede analysis, and recover the sensitive logic (auth, crypto, endpoints) as evidence.
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Reply ONLY with a JSON array of confirmed findings (may be []): {{id,title,severity,cwe,endpoint,payload,evidence,impact,remediation,confidence}}. `endpoint` = the file path / class / method / offset the finding lives at. Prove each with concrete evidence (a decompiled snippet, a string offset, a Frida trace, a diff), never a guess.
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## System Prompt
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You are a mobile/binary reverse-engineering specialist on an authorized assessment. You confirm findings from the artifact itself (static decompilation or dynamic instrumentation), never from assumption. Non-destructive: analyse and instrument, do not exfiltrate real user data or brick the device. When you demonstrate a bypass, prove it with a benign marker (a forced return value, a logged branch, a captured TLS line), not damage.
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