PhantomYerra
Capabilities

Cross-file taint analysis that tracks untrusted data across 3+ function call hops — from user input through validators, transformers, and business logic layers to dangerous sinks. Covers 20 source types (HTTP params, cookies, headers, file content, env vars, DB records, RPC responses) and 25 sink types (SQL exec, command execution, file write, template render, eval, deserialization, redirect, SSRF).

Identifies timing window vulnerabilities in concurrent code paths: TOCTOU (time-of-check to time-of-use) file and resource races, mutex misuse patterns, broken double-checked locking, and unsynchronized shared-state access. Generates PoC scripts demonstrating the race window and its exploitable impact.

Detects cryptographic implementation flaws that enable padding oracle attacks, timing oracles, nonce reuse vulnerabilities, and block cipher mode weaknesses. Covers deterministic encryption patterns, nonce reuse in authenticated encryption, and PKCS#1 v1.5 RSA implementation patterns that enable adaptive chosen-ciphertext attacks.

Traces the complete authentication and authorization chain to surface logic-layer bypasses: JWT algorithm confusion (none bypass, asymmetric-to-symmetric key confusion), session fixation patterns, insecure direct object reference (IDOR) across every object type, and missing function-level authorization checks. Evidence-gated — every finding includes a confirmed reproduction path.

Identifies unsafe deserialization entry points across multiple serialization formats in Python, Java, PHP, and Ruby, then traces the available class graph to find gadget chains — sequences of existing application classes that, when chained, achieve arbitrary code execution. Automatically generates PoC payloads for confirmed chains.

Detects supply chain attack patterns including typosquatting (edit-distance matching against the full registry of known-good packages), known-malicious package database lookups, wildcard version pins that allow silent major-version upgrades, install-time script detection, and behavioral anomalies in post-install hooks.

Runs 5 adversarial AI passes over target code, each simulating a different world-class attacker mindset: business logic reasoning (finds policy-level flaws that no signature can catch), parser differential analysis (discovers inconsistencies between layers that smuggling and injection rely on), trust boundary mapping (finds all points where untrusted data crosses a trust boundary without proper validation), state machine analysis (identifies illegal state transitions that enable privilege bypass), and type confusion hunting (discovers implicit type coercions that lead to exploitable behavior).

Deep bytecode analysis of Android application packages — detects dynamic class loading patterns that bypass static analysis, custom TrustManager implementations that disable SSL/TLS certificate validation, weak symmetric encryption usage, and obfuscated method invocation patterns used for code hiding.

Enumerates the exported component attack surface (Activities, Services, Broadcast Receivers, ContentProviders) and fuzzes them with malformed, oversized, and type-confused Intent data. Detects ContentProvider SQL injection through projection and selection clause manipulation, and deep-link URI injection through malformed scheme handling.

Analyzes WebView configurations and JavaScript bridge interfaces for exploitation paths: JavaScript interface exposure to untrusted origins, WebView sandbox escape through file scheme access, URL scheme injection attacks, and insecure content loading patterns that allow malicious web content to interact with native application functionality.

Identifies inter-process communication security violations: Binder interfaces missing proper permission enforcement, ContentProvider path traversal vulnerabilities allowing unauthorized file access, and PendingIntent hijacking — where an improperly protected PendingIntent can be captured and used by a malicious application to perform privileged operations.