Detecting Policy Violation Attacks That Precede Account Takeovers: YARA‑style Rules for Identity Logs

Hook: Stop account takeovers before the takeover — detect the policy‑violation social engineering signs

If your SIEM only looks for the final credential theft or MFA bypass, you’re already late. In 2026 attackers increasingly chain policy‑violation social engineering—messages that coerce users to break corporate policy—into a sequence of low‑signal identity events that precede account takeover. This guide gives security engineers and SIEM authors YARA‑style rules, Sigma translations, and concrete SIEM queries you can deploy now to catch the early behavioral indicators and automate containment.

Why this matters in 2026: new trends and attacker evolution

Late 2024 through early 2026 saw a sharp rise in credential‑adjacent attacks where the attacker’s first objective is not the password but to manipulate victims into violating policy: changing recovery email addresses, installing unauthorized OAuth apps, approving OAuth consent screens, or modifying mailbox rules. These techniques are faster, blend into normal activity, and bypass many legacy detections that focus on brute force or single suspicious logins.

Key 2025–2026 trends that make this a priority:

• Widespread AI‑driven social engineering: attackers generate realistic, contextually accurate “policy” messages that pass casual inspection.
• OAuth consent phishing & malicious app registrations increased across LinkedIn, Google Workspace, and Microsoft 365.
• Identity provider (IdP) logs became richer but noisier — so detections must be behavioral and chained, not single‑event rules. See modern cloud-native observability approaches for collecting and normalizing diverse telemetry.
• Industry adoption of passkeys and FIDO increased, but recovery flows and secondary controls remain exploitable.

Overview: What are policy‑violation attacks and the early signals to hunt for?

Policy‑violation attacks are social engineering campaigns designed to get employees to violate a corporate policy that then gives attackers an advantage: resetting MFA, adding a forwarding rule, installing a phishy OAuth app, or changing corporate contact details. The signature of these attacks is a chain of low‑severity identity events that, when correlated, form a high‑confidence pattern.

Typical early signals

• Unsolicited messages (email/DM) containing policy keywords + external links: "verify policy", "suspend account", "prevent termination".
• Profile/contact edits: change of recovery email, phone number, or job title that would affect self‑service recovery.
• OAuth app consent grants or suspicious app registrations that request elevated scopes.
• Mailbox rule creation (forwarding, deletion rules) or unusual inbox filters.
• Conditional access/passive device enrollment events that add a new device or revoke existing device controls.
• Burst of connection requests/messages to peers or org contacts — lateral social engineering.

Detection philosophy: behavior + chain context, not single events

To detect these attacks you must:

• Aggregate diverse identity logs (IdP, OAuth, email gateways, mailbox audit, conditional access, endpoint auth) into a normalized stream — consider edge observability patterns and centralized pipelines for low-latency enrichment.
• Define short behavioral windows (5–30 minutes) and scoring that increase confidence as events chain.
• Enrich with threat intel (phishing URLs, OAuth app reputations, known malicious domains) and organizational context (role, privileged status, recovery methods). Research on how attackers weaponize external domains is useful here (domain reselling & weaponization).
• Automate staged response — alerts, step‑up authentication prompts, session revocation, and human verification depending on score.

YARA‑style rules for identity logs: structure and examples

YARA is file‑centric, but the pattern‑matching, boolean composition, and meta fields are perfect for an expressive signature language against structured logs. Below are YARA‑style rules adapted for identity events — rules you can translate into Sigma or native SIEM queries.

Rule structure (conceptual):

• meta: rule id, author, severity, description
• strings: regex or token matches for event fields (email body, app name, URL)
• conditions: boolean composition with time windows and counts

Example 1 — OAuth consent + policy keyword in inbound message (high confidence)

<rule id="YID‑PolicyOAuth1">
meta: author = "secops@example.com" severity = high description = "OAuth consent granted shortly after policy‑tone message"
strings:
  $policy_keyword = /\b(policy|suspend|verify|compliance|terminated?)\b/i
  $suspicious_domain = /(ex‑company|verify‑secure|support\-verify)\.(com|work|io)/i
conditions:
  anyof($policy_keyword) in email.body and
  oauth.grant.event == true and
  oauth.app.name matches $suspicious_domain and
  oauth.grant.timestamp within 15m of email.received.timestamp
end

Translation notes: implement by correlating email gateway logs and IdP OAuth logs; 15m window is tunable.

Example 2 — Mail forwarding rule creation + profile change (medium confidence)

<rule id="YID‑MailboxForwardThenProfile">
meta: author = "secops@example.com" severity = medium description = "Mailbox forward created after recovery contact edit"
strings:
  $forward_create = event.type == "MailboxRuleCreate" and event.rule.action == "Forward"
  $recovery_edit = event.type == "UserAttributeChange" and (field == "recoveryEmail" or field == "recoveryPhone")
conditions:
  $forward_create and $recovery_edit within 10m
end

High value if user is privileged; escalate automatically for admins.

Example 3 — Bulk 'policy' DMs sent to peers by same user (early lateral social engineering)

<rule id="YID‑MassPolicyDMs">
meta: author = "secops@example.com" severity = medium description = "User sending policy‑tone DMs to multiple internal peers in short time"
strings:
  $policy_keyword = /\b(policy|security update|account verification|HR compliance)\b/i
conditions:
  count(messages where sender == user.id and body matches $policy_keyword within 30m) > 5
end

Concrete Sigma rules and SIEM translations

Sigma is portable and a great bridge from YARA‑style detection to SIEM search languages. Below are Sigma‑style rule descriptions and direct query examples for Splunk, Elastic, and Microsoft Sentinel.

Sigma concept: OAuth consent after external 'policy' email

Sigma fields: email.subject, email.body, oauth.app_name, oauth.grant_time, host.user, user.department

Splunk SPL (example):

index=office365 OR index=cloud_idp (sourcetype=email_gateway OR sourcetype=oauth_events)
| eval is_policy=if(match(lower(body),"\b(policy|verify|suspend|compliance|account termination)\b"),1,0)
| transaction user maxspan=15m startswith=is_policy=1 endswith=eventtype=oauth_grant
| search eventcount>1 oauth_grant=1 is_policy=1
| stats values(oauth_app) as apps, count by user, earliest(_time) as start, latest(_time) as end
| where count>1

Elastic KQL (example)

index: "idp-*" or "email-*"
"body" : /(?i)\b(policy|suspend|verify|compliance|account termination)\b"
| join kind=inner type: oauth_grants on user
| where oauth_grants.@timestamp >= email.@timestamp and oauth_grants.@timestamp - email.@timestamp < 900000

Microsoft Sentinel (KQL) — Mailbox rule + recovery change

let mailEvents = OfficeActivity
| where Workload == "Exchange" and Operation == "New-InboxRule"
| project TimeGenerated, UserPrincipalName, RuleName, Details;
let userMods = AuditLogs
| where ActivityDisplayName contains "Update user" and TargetResources has "recoveryEmail"
| project TimeGenerated, TargetUser = tostring(TargetResources[0].displayName), ModifiedBy;
mailEvents
| join kind=inner (userMods) on $left.UserPrincipalName == $right.TargetUser
| where TimeGenerated - TimeGenerated1 < 10m

Tuning for noise reduction and false positives

These detections are inherently behavioral and therefore sensitive to normal business activity. Use this tuning checklist:

1. Whitelist business flows: known automated OAuth apps, MDM provisioning IPs, and HR renewal campaigns.
2. Risk‑based thresholds: require two different event families (e.g., email + OAuth or mailbox rule + profile change) before high severity.
3. Contextual enrichments: user role, department, privileged status, recent travel, and known vendor interactions.
4. Sampling and seasonal baselines: use 30‑ and 90‑day baselines to identify legitimate campaign spikes.
5. Feedback loop: create a case‑closure tag for analysts to label detections as true/false and retrain automated scoring.

Automated containment playbook (recommended)

Once a chained detection reaches your defined confidence score, execute a staged automation playbook to balance speed with caution:

1. Enrich and confirm: fetch recent sign‑ins, device list, and active sessions.
2. Automated step‑up: prompt the user for step‑up authentication (interactive), issue a temporary block on new sessions if no response within X minutes. Consider modern micro-auth adoption patterns when designing step-up flows.
3. Containment actions by score:
   • Medium: disable OAuth app, quarantine suspicious email, create ticket for human review.
   • High: revoke all refresh tokens, sign the user out of all sessions, block OAuth consent for the app, and force password reset or recovery flow with manual verification.
4. Notify and remediate: alert the security team + IT to contact the user directly via an out‑of‑band channel (phone or direct IT portal).
5. Post‑incident actions: forensic capture, update detection rules with indicators, and notify legal/PR where required.

Detection coverage matrix: what logs to collect and why

To implement these detections you need a minimum set of identity‑centric telemetry sources:

• IdP authentication logs (Azure AD, Okta, Google Workspace): sign‑ins, conditional access, token grants.
• OAuth/OIDC app grant logs: consent approvals, client IDs, redirected URIs.
• Email gateway and secure web gateway logs: inbound messages with URLs, sender reputation, delivered/blocked state.
• Mailbox audit logs (Exchange, Google Vault): inbox rule create/delete, delegation changes, forwarding.
• Directory change logs: user attribute edits (recovery info, job title, manager).
• Endpoint MDM/Intune logs: device enrollments and new compliant devices added.
• SSO session logs: refresh token issuance, session duration aberrations.

Testing and validation: how to safely simulate policy‑violation attacks

Good detection needs adversary emulation. Build a safe, repeatable test suite:

1. Design scenarios: OAuth phishing + policy email; mailbox forward + recovery email change; mass internal DMs triggering secondary social engineering.
2. Create test accounts with non‑privileged and privileged roles to evaluate detection sensitivity.
3. Run in a controlled environment (staging tenant) or use red team engagements with documented scope and rollback procedures. For scale testing and data collection decisions, consider cost/performance tradeoffs discussed in serverless vs. dedicated crawlers.
4. Measure detection: time to detection, false positive rate, analyst time per incident.
5. Iterate rules: adjust regexes, time windows and enrichment sources based on observations.

Case study: LinkedIn policy‑violation campaigns in early 2026 (brief)

In January 2026, public reporting highlighted large policy‑tone campaigns across platforms where attackers coaxed users into ... (reporting truncated)

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