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Honest comparison

PhantomYerra vs Mythos AI

Mythos AI is an AI-cyber assistant: point it at code and it narrates a plausible exploit. PhantomYerra is a deterministic SAST scanner that finds and proves the same vulnerability classes across your whole tree - offline, reproducible, with a file and a line for every finding.

Verdict: for discovery at scale, PhantomYerra wins - it scans the entire repository deterministically, locates each issue to a line, gates it through an abstract interpreter, and rolls it into a compliance report. Mythos AI is strong at explaining a single vulnerability in plain language once you already know where to look. PhantomYerra layers that same narrative on top of a real, reproducible finding.
200
zero-day rules, always on
16
languages with zero-day rules
0
false positives on clean corpora
$0
per-scan cost, deterministic core
Two approaches to the same bug

Narrated vs source-traced

The difference is not intelligence, it is evidence. One describes; the other locates and proves.

🧠 Mythos AI - prompt narration
> "Review this handler for vulns" This code may be vulnerable to SQL injection if id comes from user input. Consider a parameterized query and validating the input. (no file, no line, no proof; varies each run)
⚡ PhantomYerra - deterministic finding
CRITICAL CWE-89 SQL Injection src/api/users.c:142 source req->id (HTTP param) sink sqlite3_exec(db,q):147 MISRA C:2023 · confidence 0.94 AI review CONFIRMED exploitable
Dimension by dimension

Side by side

CapabilityPhantomYerraMythos AI
Zero-day discovery on every scanYes, always-on suitePrompt-driven
Source-traced finding (file · line · sink)YesNarrated
Reproducible / deterministic outputSame input, same findingsNon-deterministic
Runs fully offline / air-gappedYes, pure-PythonCloud LLM
Whole-repository coverageEvery file, every commitWhatever you paste in
Languages with zero-day rules16Prompt-limited
Memory-corruption primitives (UAF, OOB, type confusion)Dedicated rulesDescribed
Deserialization gadget-chain discoveryYerraGadgetHunterDescribed
False-positive rate on clean code0 on clean corporaHallucination risk
Findings roll into compliance evidence (CRA, etc.)YesNo
Exploit-chain narrative for confirmed findingsAI on top of real findingsYes
Cost to scan an entire repo$0 deterministic corePer-token

Where PhantomYerra wins

  • Discovery, not Q&A. It scans the whole tree on every commit instead of waiting for you to paste a snippet and ask.
  • Deterministic. The same code produces the same findings every run, which is essential for CI gates and audit evidence.
  • Source-traced. Every finding has a file, a line, a source-to-sink trace and an abstract-interpreter justification.
  • Offline. Pure-Python, zero telemetry, air-gap friendly. Your code never leaves the host.
  • Compliance-ready. Findings export straight into EU CRA, OWASP, CWE, MISRA and CERT appendices.

Where Mythos AI is strong (honestly)

  • Plain-language explanation. It is good at describing a single vulnerability conversationally once you point at it.
  • Open-ended reasoning. A chat interface can explore "what if" questions that a fixed rule set will not phrase.
  • We use AI too, on real findings. PhantomYerra layers an AI exploit-chain narrative on top of a confirmed, reproducible finding, so you get the explanation without the hallucination risk.

PhantomYerra finds the vulnerability across your whole tree first, deterministically, offline, with a line-level location, and then layers an AI narrative on top of a real, reproducible finding. The two are complementary; only one of them is reproducible enough to gate a release.

Get PhantomYerra Zero-day discovery suite → vs GPT-5.4 Cyber →