vuln-intel

Most CVE tools hand your agent raw data. This one ranks it by what's actually being exploited, finds bugs by mechanism, catches the CVEs your agent invents — and transfers attack mechanics from ~14,000 disclosed bug-bounty reports.

A curated, hosted MCP corpus: ~364,000 CVEs, fused from NVD + CISA KEV + FIRST EPSS + OSV/GHSA + CISA Vulnrichment (SSVC), plus a mechanic layer distilled from ~14,000 disclosed, paid bug-bounty reports — refreshed daily. Not another live-API wrapper.

Free. Get a key: enter your email and your personal key is sent over. Tell me what you are hunting.

Why this, and not the next CVE wrapper

Most CVE MCP servers are thin wrappers: at query time they fan out to the same free public APIs and hand back whatever comes out. This is different in five concrete ways.

• A curated, embedded corpus, not a live proxy. One record per vulnerability, fused across NVD, OSV/GHSA and KEV through an identity graph, with roughly 2,100 vendor-qualified product aliases and embeddings for semantic search. Queried locally, ranked consistently.
• SSVC as priority, not just CVSS. Priority is exploitation-first: CISA SSVC (active / automatable / total impact), KEV and EPSS drive a P1 to P4 ranking. The wrapper MCPs do not carry SSVC at all.
• Search by mechanism, not keywords. Semantic search finds the same bug class across different products. Keyword tools structurally cannot.
• It fact-checks the agent. verify_cve_claim catches invented CVEs and wrong attributes. This is the one that matters most right now, with hallucinated AI bug reports flooding triage, and effectively no other CVE MCP does it.
• Right vendor, and no false zeros. "GitHub Enterprise" resolves to the right vendor, not every product that ships an "enterprise_server." A product it cannot resolve returns resolved: false with suggestions, never a silent 0 that reads as "not affected." A false zero is the worst answer a security tool can give.

The hard part of AI-assisted security is not finding CVEs. It is triage, prioritization, and false positives. That is what this targets.

Everything below is real output from the live server, trimmed only for length.

It fact-checks your agent

The differentiator that matters most. Your agent cites CVE-2025-99999:

verify_cve_claim("CVE-2025-99999")
  ->  exists: false    "No record in NVD / OSV / GHSA. Likely hallucinated or not-yet-published."

Or it gets the details wrong. Claim: "CVE-2021-44228 is a medium-severity Apache Struts bug, and it is not exploited."

verify_cve_claim("CVE-2021-44228", product="Apache Struts", severity="medium", exploited=false)
  refuted  "not exploited"          ->  in CISA KEV (added 2021-12-10)
  refuted  "severity medium"        ->  actual CVSS 10.0, P1
  refuted  "affects Apache Struts"  ->  no product matching "Apache Struts"

The other feeds hand your agent data. This one tells you when the agent is wrong, before it reaches a report.

Priority is exploitation-first (SSVC, KEV, EPSS), not CVSS

check_technology("GitLab") returns 792 CVEs for the product, de-duped and ranked so the exploited ones float to the top:

P1  KEV  EPSS 99.8   CVE-2023-7028   account-takeover: password-reset email sent to an attacker address (CVSS 10)
P2  KEV  EPSS 98.5   CVE-2021-39935  unauthenticated SSRF via the CI Lint API
P3       EPSS 99.7   CVE-2023-2825   unauthenticated path traversal, arbitrary file read (CVSS 10)
P3       EPSS 99.7   CVE-2022-2992   authenticated RCE via the GitHub import API (CVSS 9.9)

Names that map to more than one vendor are flagged ambiguous (here, gitlab vs a jenkins plugin) and kept separate, never silently merged. enrich_cve then gives you the full SSVC picture for any one of them:

enrich_cve("CVE-2024-3400")   PAN-OS GlobalProtect
  P1  KEV  CVSS 10.0  EPSS 99.95    unauthenticated command injection -> root RCE
  SSVC          exploitation=active   automatable=yes   technical_impact=total
  Metasploit    exploit/linux/http/panos_telemetry_cmd_exec  (rank: excellent)
  Public PoC    44 repos   (h4x0r-dz 162 stars, W01fh4cker 90 stars, ...)

Turn recon into a dig-order

hunt_plan(["craftcms 4.4", "nginx", "keycloak"]) ranks your stack by its most-exploitable bug and names where each component historically bleeds:

#1  craftcms 4.4    97 CVEs, 12 high-risk
    recurring_loci   CWE-94 code injection x7 (2 exploited in the wild)  ->  probe template / eval surfaces first
    dig here
       P1 KEV EPSS 99.8   CVE-2025-32432   unauthenticated RCE (CVSS 10)        your 4.4 is AFFECTED, fixed in 4.14.15
       P1 KEV EPSS 99.9   CVE-2024-56145   RCE when register_argc_argv is on    AFFECTED, fixed in 4.13.2

#2  nginx           HTTP/2 Rapid Reset CVE-2023-44487 (KEV)
#3  keycloak        recurring_loci CWE-287 auth x12.  OIDC request_uri SSRF CVE-2020-10770

It does not just list CVEs. It names the bug class a product family keeps failing at, ranked by real exploitation, and tells you whether your version is in range. Where to look, and what shape to expect.

Search by mechanism, across products

find_similar_vulns(cve_id="CVE-2021-44228"), "what else works like Log4Shell":

sim 0.88   CVE-2021-44832   Log4j2 JDBC Appender, JNDI LDAP RCE
sim 0.82   CVE-2022-40145   Apache Karaf, code injection via an attacker-controlled JNDI URL
sim 0.79   CVE-2022-34916   Apache Flume, JNDI LDAP RCE via a JMS source

The same JNDI-injection mechanism, surfaced across different products. A keyword search for "log4j" never finds Karaf or Flume. Or search a concept directly, search_vulns("SAML SSO authentication bypass"):

P3      CVSS 9.1  CVE-2024-9487   GitHub Enterprise: SAML SSO bypass via signature verification
P3      CVSS 9.8  CVE-2025-25291  ruby-saml: auth bypass via a ReXML / Nokogiri parser differential
P1 KEV  CVSS 9.8  CVE-2025-59718  Fortinet FortiOS / FortiProxy: signature-verification bypass

See what is being exploited right now

find_recent_high_risk(days=7), run live today:

P1 KEV CVSS 10.0  CVE-2026-10520  Ivanti Sentry: unauthenticated OS command injection -> root RCE
P1 KEV CVSS 9.3   CVE-2026-50751  Check Point: IKEv1 auth bypass, remote-access VPN without a password
P2 KEV CVSS 8.8   CVE-2026-11645  Chrome V8: out-of-bounds read/write -> sandbox escape RCE

Median time from disclosure to in-the-wild exploitation is now days, not months. The Ivanti bug above carried a CISA remediation deadline in the same week it landed. corpus_stats right now: ~364,800 CVEs, ~1,630 KEV entries, data under a day old — these figures are point-in-time and drift daily, so call corpus_stats yourself for the live count rather than trusting the numbers on this page.

Transfer attack mechanics from disclosed bug-bounty reports

Beyond CVEs, the corpus distills ~14,000 disclosed, paid HackerOne reports into product-agnostic attack mechanics — each bug's source → sink → trigger → preconditions, de-anchored from the product it was filed against. The premise: a vulnerability is a transferable mechanism, not a property of one product — so a move that paid on one stack is a checklist item on the next.

find_attack_approaches(query="ssrf reaching cloud metadata") — the human moves that transferred, novelty-ranked, each tagged with live program-actionability:

Reddit         SSRF   preview_url fetches an unfiltered URL → returns metadata    program_active, pays
U.S. DoD       SSRF   /download-url?url= fetches AWS instance metadata            program_active
Concrete CMS   SSRF   DNS-rebind bypass → AWS IAM creds from the metadata svc     program_active

• find_continuations(position=...) — matches your accumulated attacker position mid-hunt to the next moves real reports played from a similar spot. Every move is status: UNVERIFIED with a decisive_check to run on the target — a legal move, never a confirmed bug.
• assist_submission(finding=...) — a grounded submission brief from the closest paid precedents (validity, what's novel, an escalation playbook), with every cited report validated against the corpus (citations_grounded) so it can't smuggle a fabricated precedent.
• program_outcome_prior("hackerone:gitlab") — the bug classes that historically landed on a program, with lift over base rate (GitLab: SSRF 3.4×, SQLi ~never).

Honest about what this is: it primes and grounds a human hunter — it surfaces the move and the precedent. It does not find the bug for you; the target decides whether the move survives, and that's a step you still run.

The whole point: a memory of mechanics to borrow and run — not a CVE lookup

The moat is three things a stateless model cannot self-generate, and this corpus holds all three:

• Watched over time — priors, temporal drift (Log4Shell's affected set kept growing +1,200 days after publish), score stability. Your model has a training-cutoff snapshot; this has the trajectory.
• Seen many — every disclosed mechanic and CVE mechanism, embedded, so the same bug class transfers across products a model would never connect: Log4Shell's JNDI lookup → Apache Karaf, Flume; an SSRF that paid on Reddit → the move to try on the next target.
• Live-fused recon — observe recovers a host's real backend from its JS bundles and joins it to the corpus on the spot.