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Mythos finds the flaw. Contrast stops the exploit.

Prepare your applications for AI-built exploits.

See what Contrast can do
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<$2000

Anthropic's reported cost to build a working exploit for one long-hidden zero day 1

73%

Mythos success rate on expert-level hacking challenges 2

31%

Of breaches start with vulnerability exploitation 3

5

Days is the median time from vulnerability disclosure to known exploitation and it's getting faster 4

Mythos will not just attack your apps. It will bury your team in AI vulnerability findings.

The instinct is to fight AI exploitation with more AI scanning. That sounds logical. It also creates a bigger operating problem.
BIGGER BACKLOG

AI scanning can produce thousands of findings.

Across large applications and portfolios, most findings compete for the same limited engineering time. More findings do not mean less risk.

UNTRUSTED RESULTS

AI scan results shift from run to run.

Teams lose time reopening, rechecking, and re-arguing findings instead of reducing risk. If the result cannot be reproduced, it cannot drive a reliable remediation program.

NO RUNTIME EVIDENCE

A scan cannot show what is actually happening.

It cannot show whether vulnerable code actually ran, whether an exploit reached it, whether sensitive data is exposed, or whether attackers are probing it now. Runtime evidence can.

AI scanning sounds like the answer. The numbers say otherwise.

Contrast Labs tested pure AI scanning on real enterprise applications. At scale, three costs exploded: API spend, triage labor and trust.
$200K/year to scan one 1.8M-line application

Running a pure AI scan multiple times a day on one hardened enterprise Java application costs about $200,000 per year in API fees before a single engineer reviews a finding.

At portfolio scale with multi-agent scanning, API costs can reach $2 million to $5 million per year.

$6.4M in triage labor across a 50-repo portfolio

Quarterly scans across 50 repos can produce hundreds of thousands of findings. At 30 minutes per finding, that becomes $6.4 million in engineering time.

The scanner ran. The security posture did not move.

Only 6% of findings reproduced across all three scanners

Contrast Labs tested three AI scanners against the same codebase. Only 6% of findings were flagged by all three.

A control that returns different results on every run is not a control.

Proof. Runtime. Protection.

"Finding flaws is a solved problem. Defense is not. Defense requires evidence from the running application." Dave Lindner, CISO, Contrast Security

Find what matters. Block what executes. Fix what counts.

Observe

Know what is actually exploitable.

  • Contrast observes application behavior from inside the running app. It shows which code paths execute, which vulnerabilities are actually reached, which data flows are active, and which risks have real blast radius.

Protect

Block exploits before the patch ships.

  • Patching takes time. Attackers do not wait. Contrast blocks attacks at the point of execution, inside the application, regardless of whether the vulnerability has a CVE.

Fix

Prioritize the issues that matter now.

  • Contrast adds runtime evidence to remediation. Teams can separate theoretical findings from exploitable risk, active probing, sensitive asset exposure and attacks that need immediate action.

Why more AI scanning is not enough

Naomi Buckwalter, Product Security, Contrast Security

When organizations learn about Mythos, the first reaction is often to run more AI scans. Naomi Buckwalter explains why that response creates more findings than protection, and why runtime evidence changes the security workflow.

Naomi Buckwalter vibe coding app

Go deeper

Read the Mythos solution brief for the full breakdown of AI-built exploits, the cost of AI scanning at scale, and how Contrast combines AST and ADR to protect applications while teams fix what matters. Implementing a robust application security strategy requires defense based on evidence, not noisy findings
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Know what can actually be exploited

See which risks are actually reached, which attacks are active and where Contrast can protect you before the next patch ships.

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FAQ

  • It is real, but the bigger change is economic. Mythos did not invent exploitation. It made exploit development cheaper, faster, and easier to scale.

    That matters because attackers do not need perfect automation. They need enough automation to find more weak points than your team can triage.

  • No. AI scanning is too expensive and inconsistent to run as your daily control.

    Use AI scanning selectively for deep analysis on critical applications. Use deterministic testing and runtime protection for the security work that has to happen every day.

  • No. Scanning helps find possible issues. It does not protect the application while teams validate, prioritize and patch them.

    Runtime defense closes that exposure window by observing active attacks and blocking exploits inside the running application.

  • Contrast does not ask AI to guess what matters from millions of lines of source code.

    Contrast first observes the running application: code paths, data flows, HTTP requests, vulnerable execution, and attack behavior. Then AI can use that verified evidence to help generate targeted fixes.

  • Do not lead with backlog size. A big backlog does not tell the board whether the company can withstand an active attack.

    Report on exposure windows, active exploit attempts, mean time to contain, and whether high-risk applications have runtime protection while fixes are underway.