Prioritization based on your environment
CVSS scores severity in a vacuum. It doesn't know if a working exploit exists, if a threat actor is running it in active campaigns, or if the asset is even reachable from outside your network. Every team reconciles that gap by hand, every week: CVSS, KEV status, EPSS score, threat intel, asset criticality, compensating controls, just to get a call you'd stand behind.
When those SLA numbers feed a SOC 2, PCI, or DISA STIG attestation, a backlog you can't explain is audit risk.
Mallory helps you make that call. It correlates the vulnerability intelligence with asset context and shows you which vulnerabilities to prioritize and why. Less work, less noise, and an evidence trail your team can verify.
See how Mallory can help you prioritize exposures. No credit card.
Start your 14-day free trialHow it helps you score a finding
When Mallory performs a vulnerability investigation, it correlates relevant threat intelligence to asset context from your environment, so you can answer three questions:
- Does a working exploit exist?
- Is a threat actor actively running it in campaigns right now?
- Is the asset actually reachable, given your environment and compensating controls?
Clear all three and a finding moves up. Miss one, whatever the CVSS score, and it moves down or off the list.
Check 1
Does a public exploit exist?
Check 2
Is a threat actor actively using it?
Check 3
Is the asset actually exposed?
↓ correlated ↓
Four components run this:
- Intelligence graph: vulnerabilities, threat actors, malware, exploits, breaches, attack patterns, and observables, correlated across 6,000+ sources, plus whatever a customer adds
- Attack surface ingestion: asset and exposure data from the customer's own environment
- Agent harness: the reasoning layer connecting the two
- Context monitors: re-runs the correlation as new intel lands, so priority updates the day a PoC ships, not at next scan
Nothing hidden in the score
CVSS, KEV, EPSS, threat intel, asset criticality, exposure, compensating controls: every input stays visible on the finding. Nothing gets collapsed into a single number you have to take on faith. You can point to it in a workshop or an audit.
Feeds the stack you already run
Mallory can work bi-directionally with your existing security technologies via API or MCP. Examples of where Mallory can pull in context from your environment:
- Vulnerability management
- Cloud security / CNAPP / CSPM
- EDR
- Identity
The bi-directional integrations enable the ability to:
- Route a prioritized finding to the right owner with evidence and remediation steps attached
- Open a ticket in ticketing systems: ServiceNow Vulnerability Response, Jira
- Escalate anything past SLA to the owner's manager
- Push a standing summary of new findings to collaboration: Slack
Specifics for a technical evaluation
| Prioritization inputs | CVSS, KEV, EPSS, threat intelligence, asset criticality, exposure, and compensating controls, each visible on the finding |
|---|---|
| Intelligence sources | 6,000+ ingested sources spanning CVEs, exploit code, threat actor and campaign activity, malware, and breaches; customers can add their own |
| Integration model | Pulls findings from existing scanners, and pushes tickets and alerts back out; no scanner replacement, no rip-and-replace migration |
| Source tooling | Vulnerability management (Tenable, Qualys, Rapid7), cloud/CNAPP (Wiz, cloud and asset inventories) on the read side; ticketing (ServiceNow Vulnerability Response, Jira), collaboration (Slack) on the write side |
| Ongoing tracking | Context monitors re-run correlation as new exploit, campaign, or exposure intelligence arrives, not just at scan time |
Standalone deployment
Everything above describes the full deployment: Mallory's intelligence correlated against your own attack surface, with the agent harness and context monitors running on top. You don't have to start there.
The threat intelligence can be licensed and queried on its own. Point your own agent at the read-only MCP server at app.mallory.ai/api/mcp, from Claude Code, Cursor, Claude Desktop, or any MCP-compatible client, and you get the intelligence graph without adopting the harness or sending us anything about your environment. Setup is in the MCP documentation.
Deployment stays flexible from there. Mallory is hosted by default, with bring-your-own-key, bring-your-own-model, and bring-your-own-cloud options covered in the deployment documentation.