APPredator integrates local decompilers with cognitive language models to eliminate false positives in Android SAST and generate sandboxed-tested proof-of-concept exploits.
Upload an APK or XAPK and queue static + LLM analysis.
Stream logs, severity charts, and partial findings.
Edit retrieval context and rule templates.
APPredator solves the false positive problem in Android static testing by replacing raw signatures with localized context evaluation.
Rather than flagging simple APIs, APPredator feeds decompiled Java bytecode and class properties into advanced LLMs to semantically analyze whether the code path is exploitable.
Static pre-filtering, decompilation, and call-graph generation occur entirely on your local machine. Code snippets are isolated before being dispatched to your LLM API of choice.
A confirmed vulnerability is paired with a ready-to-run exploit script. The platform auto-validates these scripts locally inside a secure runtime sandbox before outputting.
The step-by-step pipeline of how APPredator converts raw binaries into actionable vulnerability reports.
The JADX wrapper converts Dalvik Executable (dex) binaries to clean Java source files. Concurrently, Apktool extracts binary resources, layout assets, and smali registers.
The local CodeFilter scans class trees to identify high-value targets (e.g. WebViews, SharedPreferences, SQLite databases) and compiled regexes, optimizing API overhead.
Traces method references (Xrefs) to construct a localized dependency map. This ensures the cognitive AI is injected with context-dense code execution blocks.
Queries cognitive LLM providers utilizing prompt templates enriched with dynamic OWASP MASVS compliance mappings and Android manifest component context.
Built-in support for industry-standard reverse engineering runtimes and cognitive language models.
Follow these standard terminal inputs to spin up the local console on your host system.