For Security Teams

Finding was the old job.
Now it’s fixing before it ships.

In a post-Mythos world, a finding that becomes a ticket is already a liability — StackHawk fixes it during the coding session.

Talk to our team

See how StackHawk works

Flowchart showing AI Coding Agent steps: Find (uncover risk), Fix (resolve findings), Verify (retest); results: in minutes, pre-commit, verified secure, under CI/CD pipeline label.

Proven at a national healthcare firm

%

More apps under test.

%

Backlog reduction.

Time to get there.

The threat model has changed

You can’t answer exponential finding with linear fixing.

Line graph showing monthly CVEs created from 2022 to 2026, with a rising trend. Key model releases from OpenAI, Anthropic, Google, and others are marked with colored dots along the timeline.

Low severity isn’t low risk

Mythos-class models chain low-severity findings into serious attack paths — severity alone can’t drive prioritization anymore.

A diagram shows four security issues: verbose error messages (low), user enumeration (low), missing rate limiting (medium), and weak session controls (medium), all leading to a critical exploit path..

AI catches what humans miss

Research shows multi-step attack chains that traditional reviews miss. StackHawk tests in-session and catches them before they ship.

Diagram with branching lines connecting to a box labeled StackHawk inside the agent, symbolizing interconnected processes or systems on a dark background.

Backlog is risk, not overhead

An unmanaged backlog is risk sitting on the books. The shift: fix everything at commit, not by severity.

A flowchart shows StackHawk inside the agent leading to CLEAN CODE and then to CI/CD PIPELINE on a dark background.

The board and execs need a plan

The testing doesn’t belong to your team anymore. That’s the point.

The exposure is growing

Mythos-class models are surfacing tens of thousands of vulnerabilities companies didn’t know they had. In regulated industries, that means diverting real resources to fix them, fast.

A diagram categorizes security risks into Known exposure (e.g., SQL injection, Cross-site scripting) and Newly surfaced exposure (e.g., missing rate limiting, user enumeration, weak session controls).

Fix more without hiring more

Most AppSec programs can find. Few can fix without a plan becoming a headcount problem. StackHawk is that plan: it fixes vulnerabilities inside the agent as code ships, so the backlog doesn’t outrun the team.

Line graph showing open vulnerabilities declining from 52K to 590 and agentic app adoption rising to 2.5K over four weeks, with a key event labeled Agentic Adoption at Week 3.

How it works inside the agent session

Talk to our team

Agent completes a feature

Wingman works inside Claude Code, Cursor, or Copilot—no new tool or context switch.

A dark interface displays a flowchart with Feature complete checked, followed by Wingman connected. Icons are shown at the top, indicating different features or tools.

Wingman boots and tests

Wingman auto-configures, starts the app locally, and tests it with real HTTP requests.

A dark-themed interface shows three statuses: Configured (checked), App running (green dot), and Endpoints tested (checked), under a section labeled Agent..

Finds, fixes, and verifies

The agent fixes vulnerabilities and verifies them before code leaves the coding session.

Flowchart with three boxes: Find: Runtime risk detected, Fix: Patch applied, and Verify: Security test passed, showing an agents process for detecting and resolving security risks.

A clean signal, verified

CI gets a clean signal; security gets an attestation for boards, audits, and renewals.

A software interface shows status updates: Verified secure, Attestation ready, and READY FOR CI/CD connected in a vertical flow, indicating progression through security and deployment stages.

Real results from a single app

Vulnerabilities found, fixed & verified.

Minutes to closed loop.

Token spend.

What Each Side Actually Gets

Security leadership

Velocity

Security stops being the reason a release slips.

Risk exposure

The backlog shrinks instead of growing.

Proof and reporting

An evidence trail that survives being asked twice.

Cost and headcount

Coverage scales with AI-generated code—without growing headcount.

Engineering leadership

Velocity

Ship on schedule with no new gate or context switch.

Risk exposure

Vulnerabilities are fixed in the same session they’re introduced.

Proof and reporting

A clean CI signal your team can trust.

Cost and headcount

No new hires to keep pace with your agents.

StackHawk Scale

Built for the Security Team

Attack surface discovery

Know which apps, APIs, and endpoints exist before rollout — your rollout order becomes a risk decision, not a guess.

Dashboard showing: 42 applications discovered, 186 APIs mapped, 8 priority apps, rollout priority list of three APIs, and a note that 27 applications are ready for review.

Coverage observability

See what’s tested, how often, and what’s fixed, by team — an exportable, board-ready view of the whole program.

A dashboard shows: Program coverage at 84% apps under test; Platform 92% covered, Payments 86% covered, Legacy apps 64% covered, each with test frequencies and bugs fixed.

Continuous attestation

Every agent-loop commit records testing and issues closed before merge—built-in SOC 2 evidence and a FedRAMP paper trail.

Flowchart showing steps: Commit Created, Runtime risk uncovered (Findings identified), Findings resolved (Fix verified before merge), and Attestation recorded (SOC 2 Audit).

Make Your Program Mythos-Ready.

This works better with your engineering counterpart in the room — security sets the program, developers run the loop. Bring both to a call.

Talk to our team

Finding was the old job.
Now it’s fixing before it ships.