AN1279: Analytic 1279
Excessive login attempts followed by success from SaaS apps like O365, Dropbox, etc.
Analyst context for executives and security teams
This analytic highlights a practical identity-risk pattern in SaaS environments: many failed login attempts followed by a successful login. For executives and security leaders, the value is not simply finding failed passwords; it is validating whether the organization can spot possible account compromise signals in core SaaS applications such as O365 or Dropbox before they become business disruption, data exposure, or incident-response escalation.
Executive priority
Prioritize this as an identity and SaaS monitoring control validation item. Leaders should ask whether high-value SaaS applications produce centralized authentication evidence, whether the SOC has thresholds and response playbooks for suspicious failure-to-success login patterns, and whether incident responders can quickly determine whether the successful login was legitimate. This supports operational resilience, audit evidence for access monitoring, and budget decisions around identity telemetry and managed detection coverage.
Technical view
The supplied ATT&CK object is a detection analytic for SaaS platforms with the description: excessive login attempts followed by success from SaaS apps like O365 and Dropbox. SOC and detection engineering teams should validate that SaaS authentication logs are collected, normalized, retained, and correlated by account, source, application, time window, and outcome. Because no official detection logic or tactic mapping is provided, local tuning is required to define what counts as excessive and to separate suspicious behavior from normal user lockouts, password resets, device changes, or misconfigured integrations.
Likely telemetry
- SaaS authentication logs showing failed and successful login outcomes
- Account identifiers and user principal names from SaaS identity events
- Source IP address, geolocation, device, user agent, and session metadata where available
- Application-specific sign-in records for SaaS services such as O365 or Dropbox when used in the environment
- Identity provider logs if SaaS authentication is federated
Detection direction
- Validate correlation for repeated failed logins followed by a successful login for the same account within a defined time window.
- Tune thresholds by user population, application, geography, and expected authentication patterns to reduce false positives.
- Review whether detection logic distinguishes human login attempts from service accounts, synchronization jobs, or legacy integrations.
- Prioritize alert context that helps triage quickly: account criticality, source changes, MFA outcome, impossible or unusual location indicators where available, and recent password reset or lockout events.
- Confirm coverage across the SaaS applications actually used by the business; the object only specifies SaaS generally and examples such as O365 and Dropbox.
Mitigation priorities
- Ensure centralized collection and retention of SaaS authentication telemetry for business-critical applications.
- Define response playbooks for suspicious failure-to-success login patterns, including account validation, session review, and credential reset decision points.
- Use identity controls appropriate to the environment, such as MFA enforcement, conditional access, account lockout policy, and strong password or passphrase requirements.
- Review service accounts and integrations so expected authentication failures do not mask suspicious user-account activity.
- Maintain an inventory of SaaS applications and confirm each has usable authentication logging for SOC and incident-response workflows.
Analyst notes and limits
No ATT&CK relationships, tactic mappings, or official detection logic were supplied. The analysis is therefore limited to the object description, platform value of SaaS, and the external reference to AN1279. The main decision value is coverage validation: can the organization observe and investigate suspicious SaaS login failure-to-success sequences?
This take does not establish attribution, active exploitation, specific attack technique mapping, or guaranteed detection. Thresholds, false-positive patterns, and response severity must be determined from local SaaS usage, identity architecture, user behavior, and available telemetry.
Analytic 1279
Excessive login attempts followed by success from SaaS apps like O365, Dropbox, etc.
How security teams should use this page
Treat this object as behavior context, not an attribution claim. Validate the related groups, software, data sources, and mitigations against official ATT&CK relationships and your own telemetry before making control-coverage decisions.
All related ATT&CK context
No relationships are available in the current normalized data for this object.
Object version and sync metadata
The fields below describe the current mirrored snapshot. When Glexia retains multiple ATT&CK source imports, you can open the table to compare the same object across releases (hashes and MITRE timestamps). For MITRE’s own release notes and roadmap, see ATT&CK resources — Updates .
Imported snapshots across ATT&CK releases (1)
| Release | Bundle imported | Object version | Modified | Status | Raw hash |
|---|---|---|---|---|---|
| 19.1 | 1.0 | Current bundle | feb435171c3c… |
Mirrored ATT&CK source object
The raw object is retained through the mirrored ATT&CK source bundle and object hash. The raw endpoint returns the exact object from the mirrored bundle when available.
External references and citations
MITRE external references are preserved separately from Glexia analysis so citations remain traceable to their original source records.
-
[1]
mitre-attack AN1279Open source URL
Source: MITRE ATT&CK®. © 2026 The MITRE Corporation. This work is reproduced and distributed with the permission of The MITRE Corporation. MITRE ATT&CK and ATT&CK are registered trademarks of The MITRE Corporation. Glexia is not affiliated with or endorsed by MITRE.