Suspicious activity triggers additional step-up

Impossible travel, rapid transaction sequences, and unusually large transfer amounts are established signals of account compromise or fraud. Without anomaly detection, an attacker who obtains a valid session token — through phishing, session fixation, or XSS — can execute transactions that would immediately appear suspicious to any human reviewer but proceed unchallenged by the application. CWE-307 (Improper Restriction of Excessive Authentication Attempts) and NIST SI-4 (System Monitoring) address active threat detection requirements. PCI-DSS Req 10.7 requires that anomalies trigger alerts. Suspicious activity step-up provides a dynamic second factor that activates precisely when risk is highest, without adding friction to normal user behavior.

Add at least two anomaly detection rules in src/lib/fraudDetection.ts — impossible travel is the highest-signal, lowest-false-positive starting point:

export async function checkSuspiciousActivity(
  userId: string,
  currentLocation: { lat: number; lng: number }
): Promise<boolean> {
  const lastSession = await db.sessions.findFirst({
    where: { userId },
    orderBy: { loginAt: 'desc' },
    skip: 1 // skip current
  });
  if (!lastSession?.location) return false;

  const elapsedMin =
    (Date.now() - lastSession.loginAt.getTime()) / 60000;
  const distanceKm = geoDistance(lastSession.location, currentLocation);
  const speedKmMin = distanceKm / elapsedMin;

  return speedKmMin > 15; // >15 km/min is physically impossible
}

When checkSuspiciousActivity returns true, call requireStepUp() before allowing any financial operation to proceed. Add a second rule (e.g., transactions above a threshold amount) to meet the two-rule minimum.

• ID: finserv-session-security.session-integrity.suspicious-activity-step-up
• Severity: low
• What to look for: Count all anomaly detection rules (impossible travel, unusual amounts, rapid sequences, failed login threshold). Enumerate each rule and its trigger threshold. Quote the actual detection logic found. Verify at least 2 suspicious activity patterns are monitored. Count all step-up triggers for suspicious activity.
• Pass criteria: At least 2 suspicious activity patterns are detected (e.g., impossible travel, unusual transaction amount). When detected, at least 1 step-up challenge is triggered. Report the count even on pass (e.g., "3 detection rules: impossible travel (>15 km/min), amount >$10000, >5 failed logins — all trigger step-up").
• Fail criteria: No suspicious activity detection (0 rules), or detection exists but does not trigger step-up (0 step-up triggers for anomalies).
• Skip (N/A) when: The application does not perform financial transactions — cite the actual project type found.
• Detail on fail: "0 anomaly detection rules — impossible-travel logins not detected, no fraud monitoring"
• Remediation: Add simple anomaly detection (in src/lib/fraudDetection.ts):

  // lib/fraudDetection.ts
  export async function checkSuspiciousActivity(userId: string, action: string) {
    const recentSessions = await db.sessions.findMany({
      where: { userId, expiresAt: { gt: new Date() } },
      orderBy: { loginAt: 'desc' },
      take: 1
    });
  
    const lastSession = recentSessions[0];
    if (!lastSession) return false; // First login, not suspicious
  
    const timeSinceLastLogin = (Date.now() - lastSession.loginAt.getTime()) / 1000 / 60; // minutes
    const distance = geoDistance(lastSession.location, currentLocation); // km
    const speedOfTravel = distance / timeSinceLastLogin; // km/min
  
    if (speedOfTravel > 15) { // Impossible travel (>15 km/min)
      return true; // Flag as suspicious
    }
    return false;
  }