When a security vendor tells you their AI is "state of the art," the only honest follow-up question is: compared to what, measured how, and show me the numbers. In the AppSec AI market, Griffin AI is one of the few systems that answers that question with a concrete harness and concrete scores. Most Mythos-class competitors answer with a deck.
This post lays out what Griffin AI publishes, what Mythos-class tools typically do not, and why published benchmarks are the single clearest trust signal a buyer can demand in 2026.
The five families Griffin AI reports
Griffin AI's public eval harness covers five families of tasks that reflect the actual day-to-day work of a security engineering function. Each has a score, a method, and a golden dataset behind it.
- Exploit-hypothesis generation: 81% agreement with human analysts on whether a finding is reachable and weaponizable in a realistic deployment.
- Remediation-PR correctness: 73% of generated patches compile and pass project test suites on first apply.
- Advisory summarization: 0.89 similarity to curated reference summaries of upstream advisories.
- Cross-finding correlation: 88% precision and 82% recall when linking findings that share a common root cause across projects.
- Adversarial resistance: 98-100% hold rate against prompt-injection, jailbreak, and data-exfiltration probes.
None of those numbers are flattering by accident. They are the result of a harness that runs on every model update, every prompt change, and every corpus refresh. When they move, we investigate. When a regression is real, we ship the fix before we ship the feature.
What Mythos-class vendors typically publish
The category we call Mythos-class (without naming any one vendor) is populated by tools that pitch an AI security copilot, an AI remediation bot, or an AI SOC analyst. The pattern we see again and again when a prospect shares their competing materials with us looks like this:
- A single headline accuracy number, usually 90-something percent, with no definition of what "accuracy" means on what task.
- Testimonials and customer logos.
- Screenshots of a chat UI producing a confident-looking answer.
- A "trust center" page that lists SOC 2 Type II and nothing about model behavior.
That is not a benchmark. That is a brochure. The information a buyer actually needs is the five-tuple: task definition, dataset, metric, score, and confidence interval. If any of those is missing, the number is not falsifiable, which means it is not a number, it is a claim.
Why published benchmarks are a trust signal
Security buyers have a long memory for vendors who promised detection coverage and delivered noise. Published benchmarks reverse the usual asymmetry in a few concrete ways.
First, they force the vendor to define the task. If a competitor tells you their AI is 94% accurate, ask them what the denominator is. The Griffin AI exploit-hypothesis number is 81% because we defined "correct" narrowly: does the model's judgment on reachability agree with a senior analyst's judgment on the same finding, given the same context. That is a harder task than "did the model say something that sounds right," which is the bar most unpublished numbers are secretly using.
Second, they expose the shape of the failure. An 81% score means we fail 19% of the time, and our release notes describe where: specifically, findings in indirectly loaded dependency graphs where the model overestimates reachability. A buyer can plan around that. A buyer cannot plan around a vendor who says "it just works."
Third, they let the market compare apples to apples. When two vendors report on the same golden dataset with the same metric, the market can do what markets do: price the difference. When one vendor reports and the other does not, the non-reporting vendor is asking for a premium on faith.
The asymmetry of "trust us"
There is a pattern in enterprise software we have watched play out for two decades: a vendor launches a category, wins early logos on narrative, and then gets undercut by a second wave of vendors who publish more and promise less. The same thing is happening in AI AppSec right now.
Mythos-class tools are early-wave. They are selling a story about agentic autonomy, about an AI that "thinks like a security engineer." The story is good. The story is also unfalsifiable until there is a harness, a dataset, and a number.
Griffin AI's bet is that buyers in 2026 are tired of unfalsifiable stories. The CISO who signed a PO for an AI SOC in 2024 and got a noisier dashboard in 2025 is the same CISO who is now asking every vendor in their stack the same three questions:
- What does your model do when it does not know the answer?
- What is your false-positive rate on findings my team has already triaged?
- Where is your benchmark harness, and can I run it myself?
The vendors who can answer all three win the renewal. The vendors who deflect the third question lose on the next budget cycle.
What a buyer should ask for
If you are evaluating Griffin AI against a Mythos-class tool, the single most leveraged thing you can do in a technical deep dive is ask for the benchmark pack. Here is the list we recommend:
- A named task definition for each claim the vendor makes. "Finds vulnerabilities" is not a task. "Generates an exploit hypothesis for a SAST finding that agrees with a human analyst" is a task.
- The size and sourcing of the golden dataset. Public CVEs? Synthetic? Internal? How many items? How was it constructed?
- The metric. Precision, recall, F1, Jaccard, BLEU, human-preference win rate, something else?
- The score, with a confidence interval or at least a sample-size caveat.
- The release cadence. When was the number last computed? On what model version? On what prompt version?
If a vendor cannot answer even half of those, you are being asked to buy an opinion, not a product.
Griffin AI's self-critique
We are not publishing these numbers because they are perfect. The remediation-PR compile rate of 73% is the one that bothers us the most internally, because the 27% that fail usually fail on legitimate project-specific build config issues rather than generation bugs. We have a roadmap to push that number above 80% by mid-year by expanding the build-context layer, and that roadmap lives on our public changelog.
The point is not that Griffin AI is flawless. The point is that Griffin AI is measurable. A tool that is measurable is a tool that can improve, regress, and be held accountable. A tool that is not measurable is a tool that is whatever the sales engineer tells you it is on the day of the demo.
The bottom line
Mythos-class competitors want you to buy the story. Griffin AI wants you to buy the harness. In a market that is about to be flooded with AI security promises, the harness is the only thing that will still be meaningful in eighteen months.
If your next AppSec AI evaluation does not include a side-by-side benchmark review, you are not evaluating. You are hoping. Hope is an expensive procurement strategy.