What an email reveals

An email address is usually the highest-value selector in an investigation: it carries a name, a domain, an account history, and a breach footprint. The beginner error is trying to prove the address exists. Existence is the least interesting thing about it, it is the hardest thing to establish passively, and the techniques that establish it are the ones that expose you. Work the structure and the footprint first; treat validation as a bonus.

Never send mail to the subject as a research technique.

Step 1 — Authorized scope

Read ../../ETHICS.md. Write down subject, objective, in-bounds selectors, out-of-bounds actions, and the governing jurisdiction. Decide in advance whether interactive probing — SMTP conversations, password-reset flows, signup-form enumeration — is authorized. It usually is not. Everything below is passive unless marked otherwise.

Done when scope is written and the interactive-probing decision is recorded.

Step 2 — Parse and validate

Three different things get called "email validation". They are not interchangeable.

Do the first two. The third — opening an SMTP session and issuing RCPT TO to see whether the server accepts the recipient — is a live conversation with the subject's mail infrastructure from your IP. It gets logged, it gets your address range blocklisted, and against a catch-all domain it is worthless: a catch-all accepts every recipient, so every address "exists". Greylisting, tarpitting, and accept-then-bounce policies produce the same useless answer. Third-party validation APIs mostly wrap the same probe; the exposure moves to the vendor, the accuracy limits do not change.

Then parse the local part. first.last, flast, firstl, f.last each imply a name and, on a corporate domain, a company-wide convention — see reference/corporate-formats.md.

Gmail normalisation matters. Gmail ignores dots in the local part and everything after a +. j.doe+news@gmail.com, jdoe@gmail.com and jd.oe@gmail.com are one mailbox. Consequences: addresses that look different in two breaches may be the same person, and a +tag frequently names the service the address was given to, which is free intelligence about where the subject holds accounts. Not every provider behaves this way — check before assuming.

Done when the address is graded valid / invalid / unknown, the normalised form is recorded, and the name hypothesis is written down.

Step 3 — Gravatar

Gravatar maps an address to a public avatar by MD5 hash of the lowercased, trimmed address. Compute the hash and request the avatar; a returned image means the address was registered with the service, and the associated public profile can carry a display name, a location, links to other accounts, and verified accounts on other platforms.

printf '%s' "jdoe@example.com" | tr 'A-Z' 'a-z' | md5sum

Free, passive, no key. Run the avatar through find-the-original-image. Treat a default fallback image as "no Gravatar", not as "address invalid".

Done when Gravatar presence is checked and any profile fields captured.

Step 4 — Where is this address registered

The honest position: reliable account enumeration by email is an oracle problem. Password-reset and signup forms disclose whether an address is registered, which is exactly why tools exist to automate them — and exactly why doing so is interactive, often against terms of service, and potentially notifying (a reset request can email the subject). Flag it, get it authorized explicitly, or don't do it.

Passive alternatives that cost you nothing:

• google-like-a-spy the full address in quotes, and the local part alone. Resumes, conference programmes, mailing-list archives, WHOIS history, and committed config files are full of addresses.
• Run what-leaked-about-you. Breach membership is the single best answer to "which services did this identity use", and it is retrospective rather than interactive.
• Push the local part into hunt-a-handle as a username seed.
• Search code hosting for the address in commit metadata via secrets-in-git-history.

Done when the service list is assembled and each entry is marked passive or interactive in provenance.

Step 5 — Read the headers, if you have the message

Only applies when you legitimately possess the message. Headers are where an email stops being a selector and becomes evidence.

Read the Received: chain bottom-up: the bottom-most is the earliest hop, and the originating host is there unless the sending platform strips it. Everything below the first server you trust can be forged wholesale.

• Message-ID — the domain part and the ID's shape often identify the sending platform or mail client even when the visible headers are cosmetic.
• Authentication-Results — the receiving server's SPF, DKIM, and DMARC verdicts. A DKIM pass is the strongest thing in the header block: it is a cryptographic signature over content, so it survives forwarding claims.
• X-Mailer / User-Agent — client fingerprint, frequently left in place by bulk-mail tooling.
• Return-Path vs From — a mismatch is normal for mailing lists and suspicious in direct correspondence.

Field-by-field guide: reference/header-fields.md.

Done when the originating infrastructure is identified or explicitly stated as unrecoverable.

Where this goes wrong

• Catch-all domains defeat verification outright. Everything validates. Detect one by testing an address you invented; if a random string validates, every result from that domain is meaningless.
• Disposable and forwarding services. Throwaway domains mean the address was never meant to persist; relay and alias services (including provider-issued private-relay addresses) mean the visible address is a wrapper around a mailbox you cannot see. Both cap how far the address can take you — recognise them early rather than burning hours.
• Role addresses (info@, sales@, admin@) belong to functions, not people. Attributing one to an individual is the most common serious error in email OSINT, and it survives into reports because it looks like a finding.
• Inferred addresses are hypotheses. Deriving j.doe@company.com from a company pattern gives a plausible address, not a real one. Label it inferred, permanently.
• Breach data is not proof of current ownership. Addresses get abandoned, recycled by providers, and reassigned to new staff at the same company.
• Forwarding is indistinguishable from forgery at a glance. Mailing lists and security gateways rewrite headers in ways that look like tampering.

Confidence grading

• Confirmed — a DKIM-passing message from the address, or the address published by the subject on a source they control, or an authenticated Gravatar profile matching a separately confirmed identity.
• Probable — MX-valid, appears in a breach alongside a corroborating selector, and the local part matches the subject's name pattern.
• Unconfirmed — inferred from a corporate format, or found in a single aggregator or combolist with no second source.
• Rejected — proven catch-all with no supporting evidence, a known disposable domain with no linked activity, or a role address attributed to an individual on no basis but the domain.

Worked example

Given j.okonkwo@northwind-eng.example. dig MX returns records at a hosted provider — the domain takes mail. Gravatar: no avatar, so no profile pivot.

Two employee addresses in a conference PDF are t.mwangi@ and s.aldridge@, so the convention is first-initial.lastname. That makes j.okonkwo@ structurally consistent — evidence about the format, not evidence the person exists. No SMTP probe is run; interactive steps are out of scope here.

The dead end: what-leaked-about-you returns nothing for the address, which initially reads as "not a real user". Re-running against the Gmail-normalised personal address recovered from a code-hosting commit gives three breaches, one of them a developer forum with the handle sunfish_ada. That handle goes to hunt-a-handle. The corporate address stays graded inferred; the personal one is the working selector.

Pivots

Legal and ToS notes

SMTP probing and password-reset enumeration are interactive, commonly prohibited by terms of service, and in some jurisdictions arguably unauthorized interrogation of a system. Get them in writing before you use them. Under GDPR and UK data protection law an email address is personal data on its own — collect only what the objective needs, store it encrypted, and set a deletion date with the case file. Message content you possess may carry separate confidentiality or privilege obligations independent of the OSINT question.

Step 6 — Report

Run write-the-intel-brief. State validity and how it was established, whether the domain is catch-all, the service list with passive/interactive provenance, breach exposure, and the identity attribution with its confidence grade.

Done when every address is marked observed or inferred, and no inferred address is stated as fact.

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Supporting file: reference/corporate-formats.md

Inferring a corporate email format

Most organisations issue addresses from a single template. Recover the template from a few known addresses and you can generate a plausible address for any employee whose name you have. The output is always a hypothesis, and must be labelled inferred in any report.

Common templates

Running example: Jane Q. Okonkwo at northwind.example.

Roughly in order of prevalence: first.last, flast, firstlast, first, f.last. Start there.

Deriving the pattern

You need two or three real addresses with known corresponding names. Sources that are public and passive:

• Press releases, investor relations pages, and regulatory filings — media contacts are almost always real, individual addresses.
• Conference programmes, academic papers, and standards documents (PDFs).
• Job postings that name a hiring contact.
• Mailing-list archives, bug trackers, and public support forums.
• Commit metadata on code hosting: corporate laptops commit with corporate addresses. Use secrets-in-git-history.
• Domain registration and technical contacts, where not redacted: use who-owns-this-domain.
• google-like-a-spy with the domain plus an @ and a common first name.

Two addresses that agree on a pattern are a weak inference. Three from independent sources is workable. One is nothing.

Complications that break the inference

Multiple domains. Large organisations run separate domains for corporate, engineering, regional subsidiaries, and acquisitions. The pattern may be consistent while the domain is not. Check MX records for candidate domains and look for which one the known addresses actually use.

Acquisitions. Staff from an acquired company often keep legacy addresses, which get forwarded. Someone's real address may follow the previous employer's convention entirely.

Collisions. Two people with the same name force a deviation — a middle initial, a number, a full first name where others get an initial. The deviation is invisible from outside.

Name normalisation. Accents, hyphens, apostrophes, and non-Latin scripts get transliterated or stripped, and different organisations do it differently. Compound and multi-part surnames may be joined, hyphenated, or truncated. Preferred names diverge from legal names ("Bob" for Robert), and the address may use either.

Aliases. Many organisations issue several addresses per mailbox — a first.last alias over an employee-ID mailbox, for example. Both work; only one appears in the directory.

Catch-all domains. If the domain accepts everything, no amount of validation will distinguish a real address from a generated one. Test with an obviously invented name before believing any verification result.

Role and functional addresses

info@, sales@, support@, admin@, hr@, security@, abuse@, postmaster@, noreply@, careers@, press@, legal@.

These belong to a function and are usually read by a shared mailbox or a ticketing system. Never attribute one to a named individual. They are still useful: abuse@ and security@ are often required to be monitored and can confirm a domain is actively administered, and the auto-reply from a ticketing system fingerprints the vendor — though soliciting one is interaction, not passive research.

Using an inferred address

Do not treat it as confirmed by an SMTP probe — probing is interactive, and catch-all domains make the result meaningless anyway. Instead, look for the inferred string appearing independently: in a breach corpus via what-leaked-about-you, in indexed documents via google-like-a-spy, in commit history, or on Gravatar. An independent appearance upgrades the hypothesis to a finding. Nothing else does.

Record inferred addresses in a separate column from observed ones. The distinction gets lost the moment they share a table, and an inferred address that leaks into a client report as fact is a serious error.

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Supporting file: reference/header-fields.md

Email header field reference

Applies only to messages you legitimately possess. Export the raw source (the "show original" / "view source" function in any mail client) rather than working from what the client renders — clients hide, reorder, and prettify headers.

Reading order

Headers are prepended as the message travels, so the file reads newest-first. Start at the bottom of the Received: block and work upward. That gives you the message's actual path in chronological order.

The trust boundary is the point where the message entered infrastructure you or the recipient controls. Everything added after that point is trustworthy; everything claimed before it is sender-supplied and can be fabricated in bulk. A forged chain is usually a block of plausible-looking Received: lines at the bottom, followed by the one real line added by the first honest relay.

Trace headers

Authentication results, in order of evidentiary weight

DKIM is the strongest. A dkim=pass verdict means a cryptographic signature over selected headers and the body validated against a public key in the signing domain's DNS. It survives relaying, and it means the signing domain genuinely handled this content. Note the d= parameter — the signing domain is often the sending platform, not the apparent sender.

SPF authorises a sending IP for the envelope domain. pass says the IP was allowed to send for that domain; it says nothing about the visible From, which is why SPF alone is weak evidence of authorship. It also breaks under forwarding.

DMARC ties the visible From domain to an SPF or DKIM pass on an aligned domain, and publishes a policy for failure. A dmarc=pass is the closest a header gives you to "this really came from that domain".

A missing Authentication-Results header does not mean the message failed checks. It usually means the receiving server did not run them.

Identity and client headers

MIME and attachment metadata

Boundary strings are generated by the composing library and are themselves a client fingerprint. Attachment filenames, Content-Type parameters, and the attachment's own embedded metadata carry more identity than the headers do — run attachments through secrets-in-file-metadata, and embedded images through find-the-original-image.

Turning headers into selectors

• Originating IP → geolocation is coarse and often wrong for mobile and corporate networks; passive infrastructure detail via find-exposed-servers, ownership via who-owns-this-domain.
• Sending domain → who-owns-this-domain and recon-a-domain-passively.
• Signing domain (d= in DKIM) → identifies the platform used to send.
• Any address in From, Reply-To, Cc → back to the main skill.

What headers will not give you

The sender's physical location. A residential IP narrows to an ISP and a broad region at best; VPNs, mobile carriers, and corporate egress make even that unreliable, and every major webmail provider now sends from its own infrastructure so the originating client IP simply is not present. If the question is "where was this person", headers are the wrong instrument.

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Supporting file: ETHICS.md

Ethics, Legality & Authorized Scope

OSINT is powerful. These skills are built for lawful, authorized, defensive work: threat intelligence, fraud investigation, due diligence, journalism, missing-persons research, penetration-test reconnaissance, and personal digital self-defense.

Every workflow skill opens with an authorized scope gate. Honor it.

The rules

1. Passive by default. Prefer observation over interaction. Never log in to, probe, exploit, or send traffic to a target's private systems without written authorization. Reading a public profile is OSINT; brute-forcing a login is a crime.
2. Stay legal in your jurisdiction. Computer-misuse, wiretap, stalking, harassment, and data-protection laws (GDPR, CCPA, etc.) all apply to research. When unsure, stop and get counsel.
3. No harassment, doxxing, or stalking. Do not use these skills to locate, intimidate, or expose private individuals for harm. Aggregating someone's personal data to threaten them is abuse, full stop.
4. Minimize and protect data. Collect only what the objective requires. Store case data encrypted, share on need-to-know, and delete when done.
5. Corroborate before you conclude. A single selector match is a lead, not a fact. Attribution requires multiple independent, corroborating sources.
6. Respect terms of service and rate limits. Automated scraping can be illegal or get you banned. Use official APIs where they exist.

Not for

Stalking, harassment, doxxing, unauthorized access, or any activity prohibited by law. If your objective is to harm a person, these skills are not for you.

By using this repository you accept full responsibility for how you apply it. The authors provide it "as is" with no warranty (see LICENSE).