Find Zero-Days Pattern Scanners Miss
Pattern-matching scanners can only find vulnerabilities someone has already reported. Safeguard's engine plus Griffin AI traces taint across package boundaries and hypothesises exploit conditions — surfacing candidate zero-days in your dependency graph before they are published anywhere else.
Why Pattern Scanners Never Find Them
Traditional SCA tools are looking for vulnerabilities that already have CVEs. A zero-day, by definition, doesn't.
CVE-Based Detection Is Inherently Reactive
Pattern scanners match your dependencies against a database of known vulnerabilities. If a vulnerability hasn't been disclosed, there's no pattern to match. You find out about the zero-day when the attacker uses it on someone else.
Cross-Package Taint Flows Are Invisible
Your HTTP handler in app.js reaches a sink in a transitive dependency 7 hops deep. No single file contains the full vulnerability. Pure static analysis in one package misses the flow that crosses package boundaries.
Pure-LLM Bug-Hunters Have 60-95% False Positive Rates
LLMs asked to 'find vulnerabilities' flag every risky-looking sink. Without structured context about reachability and exploit conditions, the output is unusable at production scale.
Triage Capacity Is Already Saturated
Security teams drown in thousands of low-signal findings. Adding more noise to the queue — even if some of it is signal — doesn't help. What's needed is high-precision candidates, not high-volume alerts.
The Engine Finds The Shape. The Model Hypothesises The Exploit.
Stage 1 — Taint Analysis
The deterministic engine walks the cross-package call graph, propagates taint from every source type to every sink, and surfaces every path that survives existing sanitizers.
Stage 2 — Griffin AI Hypothesis
For each surviving path, Griffin AI receives a structured brief (source, sink, intermediate code, version) and hypothesises the exploit class, trigger input, and CVE mapping — if one exists.
Stage 3 — Disproof & Verification
A second model pass tries to disprove the hypothesis. Only candidates that survive the adversarial check reach the review queue — keeping precision high and triage hours low.
Three Weighted Models, One Cybersecurity Brain
Every model is weighted purely on cybersecurity corpora — CVE bodies, exploit write-ups, taint graphs, advisory disclosures, malware behavioural traces — not general internet text. Each is tuned for a different point on the precision/latency curve.
They cooperate: Lion flags candidates at commit time, Eagle widens the surface across a repo, Griffin proves or disproves the hypothesis with deep reasoning.
Griffin
Deep reasoning · the hypothesis engine.
Heavyweight reasoning model. Performs deep transitive dependency analysis, hypothesises exploit conditions, runs an adversarial disproof pass, and generates remediation PRs. Best when you need a single high-precision verdict on a candidate path.
Eagle
Wide-angle triage · the surface scanner.
Fast attack-surface mapper. Sweeps the call graph, clusters taint flows, and ranks the top candidate paths in seconds so Griffin spends its budget on the right ones.
Lion
Inline · the commit-time gut check.
Tiny, distillation-trained inline model that runs in the IDE / CLI / pre-commit hook. Catches obvious vulnerable sinks and bad sanitiser usage before code reaches CI.
Inline sink + sanitiser check on the dev's machine.
Cross-package taint paths ranked across the codebase.
Deep reasoning posits an exploit chain + CWE class.
Second pass tries to refute; survivors hit the queue.
What A Zero-Day Actually Looks Like From The Queue
From First Commit To Your Queue
- t = 0Lion
Flags a risky deserialization sink in PR #4129. Inline, <80 ms, on the developer's laptop.
- t + 90 sEngine
Reconstructs the cross-package call graph for the merged commit, version-pinned.
- t + 3 minEagle
Ranks 47 surviving taint paths; 6 reach untrusted HTTP entry points.
- t + 8 minGriffin
Hypothesises an unsafe-deserialization → RCE chain for the top candidate, attaches a synthesized trigger input, maps to CWE-502 with no matching CVE.
- t + 9 minDisproof
Second pass fails to refute under the project's sanitiser config; finding lands in your review queue with full evidence bundle.
What You Actually See
Every finding lands with the full evidence bundle attached. No “trust us” verdicts.
Source → 6 hops → sink, every node version-pinned.
Mapped to the closest CWE, even when no CVE exists yet.
A synthesized payload that should reach the sink.
Every refutation the adversarial pass tried and failed.
PR-ready diff with compatibility notes for the pinned versions.
Coordinated-disclosure email pre-written, opt-in to send.
Downstream dependents affected if exploited in the wild.
Findings stay private to your tenant. Coordinated disclosure to upstream maintainers is opt-in per finding.
How A Candidate Zero-Day Reaches Your Triage Queue
Taint analysis surfaces a path from HTTP request body into a deserialization sink 6 packages deep. Griffin AI hypothesises an unsafe-deserialization pattern and maps it to a known CWE. The disproof pass attempts to refute the hypothesis with specific mitigation conditions; it cannot. The finding reaches your queue with the full taint path, exploit hypothesis, disproof attempt, and a ranked evidence bundle. If you opt in, Safeguard runs coordinated disclosure with upstream maintainers on confirmed candidates.
Find What's Hiding In Your Dependencies.
Run the engine against your codebase and see the candidate zero-days your pattern scanner never surfaced.
Self-healing security runs on Safeguard.
Your first fix PR is minutes away.
No sales call required, even your agent can complete the purchase over MCP.