On top of 24,476 pattern rules, PhantomYerra runs a language-complete zero-day detection engine on every scan - exploit-primitive and dangerous-construct patterns for memory corruption, deserialization gadget sinks, command/template injection, unsafe FFI, type confusion, TOCTOU and weak-crypto primitives - across all 16 languages, C/C++ included. A deeper 7-engine discovery suite (taint, concurrency, crypto oracle, auth-chain, gadget chains, supply chain, AI hypothesis-and-validate) augments it for web and managed languages.
YerraZeroDay engine wired into the SAST pipeline - self-filters by the scanned language, so it fires for C/C++ and every other technology you select. Deterministic and reproducible; 0 false positives on clean corpora.Each runs after the primary rule pass, contributes findings with full evidence, and never aborts the scan. This is how PhantomYerra surfaces vulnerabilities that have no CVE yet.
Follows attacker-controlled data across files and functions from source to dangerous sink - the cross-file paths single-file pattern scanners structurally cannot see.
Models shared-state access, lock ordering and check-then-use windows to surface timing bugs that only manifest under concurrency.
Detects misuse that turns crypto into an oracle an attacker can query to recover plaintext or forge tokens.
Walks the authn/authz path to find where a check is missing, reachable around, or applied to the wrong principal.
Reconstructs reachable gadget chains from untrusted deserialization to executable sinks - the mechanism behind many critical RCEs.
Inspects dependencies, lockfiles and build scripts for compromise patterns before they ship into your build.
An LLM reads your code in chunks, hypothesizes novel vulnerability classes specific to your logic, and validates each against the surrounding code before reporting.
ai_semantic_sast.py) and AST-mutation
fuzzing (yerra_ast_mutation_fuzzer.py). The deterministic six run on every scan; the
AI engine fires when AI passes are enabled, gated for reproducibility.Traditional SAST matches patterns for known bug shapes. AI-cyber tools narrate exploits but rarely ship a reproducible, source-level discovery suite. PhantomYerra does both.
| Zero-day capability | PhantomYerra | Signature SAST (Sonar/Snyk/etc.) | AI-cyber (Mythos / GPT-Cyber) |
|---|---|---|---|
| Cross-file interprocedural taint | Yes - YerraIntelliTrace | Partial / paid tier | Narrated, not source-traced |
| Concurrency / TOCTOU discovery | Yes - YerraRaceTrack | Rare | No |
| Crypto-oracle discovery | Yes - YerraCryptoSeer | No | No |
| Auth-chain bypass discovery | Yes - YerraAuthTracer | Partial | Narrated |
| Deserialization gadget chains | Yes - YerraGadgetHunter | Sink-only | No |
| Supply-chain compromise patterns | Yes - YerraSupplyWatch | CVE-DB only | No |
| AI novel-class discovery (validated) | Yes - YerraZeroDayAI | No | Hypothesis, often unvalidated |
| Reproducible without signature DB | Yes - deterministic core | DB-dependent | Non-deterministic |
| Runs offline / air-gapped | Yes | Cloud-first | Cloud LLM |
This is the same zero-day discipline behind our Mythos AI and GPT-5.4 Cyber comparisons - extended into static analysis and made reproducible across 16 languages.
AI-cyber assistants narrate plausible exploits from a chat prompt. PhantomYerra runs a deterministic, source-traced zero-day discovery suite on every scan - 200 exploit-primitive rules across 16 languages, offline and reproducible. Where they describe, we detect, locate and prove.
| Capability | PhantomYerra | Mythos AI | GPT-5.4 Cyber |
|---|---|---|---|
| Zero-day discovery built into every scan | Yes - always-on suite | Prompt-driven | Prompt-driven |
| Source-traced finding (file · line · sink) | Yes | Narrated | Narrated |
| Reproducible / deterministic output | Yes - same input, same findings | Non-deterministic | Non-deterministic |
| Runs fully offline / air-gapped | Yes - pure-Python | Cloud LLM | Cloud LLM |
| Languages with zero-day rules | 16 | Prompt-limited | Prompt-limited |
| Memory-corruption primitives (C/C++ UAF, OOB, type confusion) | Yes - dedicated rules | Described | Described |
| Deserialization gadget-chain discovery | Yes - GadgetHunter | Described | Described |
| AI hypothesis-and-validate (novel classes) | Yes - ZeroDayAI, validated in-code | Hypothesis only | Hypothesis only |
| False-positive rate on clean code | 0 on clean corpora | Hallucination risk | Hallucination risk |
| Findings roll into compliance evidence (CRA, etc.) | Yes | No | No |
| Exploit-chain narrative for confirmed findings | Yes - AI on top of real findings | Yes | Yes |
| Cost / call to scan an entire repo | $0 deterministic core | Per-token | Per-token |
The AI-cyber tools are strong at explaining a vulnerability once you point at it. PhantomYerra finds it across your whole tree first - deterministically, offline, with a line-level location - and then layers the same AI narrative on top of a real, reproducible finding.
Point PhantomYerra at your repository - the discovery engines run on every scan, fully offline, with reproducible evidence.