A2P SMS Pumping Signals: Investigate Without Blocking Legitimate Users

What SMS pumping is and why it can affect legitimate OTP traffic

SMS pumping is a form of fraudulent messaging traffic inflation. It can generate an increase in messages and raise associated costs. In an A2P one-time password (OTP) flow, an unexpected change warrants investigation, but volume alone does not identify its cause or prove fraud.

The analysis must protect two objectives at once: contain potential costs and abuse, while preserving a reasonable way for a legitimate person to complete verification. An alert is a reason to review the evidence, not an automatic conclusion about users or destinations.

Separate anomalies in volume, destinations, and behavior

Examine signals separately before combining them. An increase in requests may have legitimate explanations, such as normal service activity or an operational change. A new concentration of traffic in certain prefixes or destinations is also an indicator that needs context, not proof of fraud.

Compare equivalent periods and cohorts using the information available to your operation. Look for simultaneous changes in volume, destination distribution, request frequency, and costs. Avoid turning an isolated figure into a universal threshold: limits should be defined using your own data, service context, and risk tolerance.

• Volume: identify when the change began and whether it affects the whole service or a specific part of it.
• Concentration: observe whether the destination distribution has changed compared with a comparable baseline.
• Behavior: review whether requests depart from the flow’s normal pattern, without assuming that any single characteristic is malicious.
• Cost: compare observed spending with the corresponding volume and distribution.

Correlate requests, sessions, statuses, and costs

Build a timeline using data that is available and relevant: OTP requests, associated sessions or attempts, destination at the minimum necessary level, provider submissions, reported statuses, DLRs, and costs. Temporal correlation can help identify where an anomaly appears and whether indicators are changing together.

Define in advance what each field means in your systems and preserve its provenance. A status received from a provider, an application event, and a recorded cost may describe different stages. If records cannot be linked with sufficient confidence, document that limitation rather than presenting a correlation as causation.

• Record the time and time zone, the affected service or flow, and the analysis window.
• Distinguish requests created, submission attempts, and statuses received.
• Link costs to the period and traffic they actually represent.
• Document missing fields, logging delays, and discrepancies between systems.

DLRs and HLRs: uses and interpretation limits

A DLR should be interpreted as a reported status within the messaging chain and in accordance with the semantics documented by the provider or platform. Do not automatically present it as independent verification that a message reached the phone: the evidentiary scope depends on the route and how the status was generated.

An HLR lookup may provide network or numbering information, depending on the service available, but it does not prove consent, identity, number ownership, or guaranteed receipt. None of these indicators, on its own, proves that SMS pumping is occurring. Use them only alongside other data and state their limitations explicitly.

Proportionate controls and time-limited pauses

Choose a response based on the strength and scope of the evidence. Start by checking that the alert is not caused by a measurement error or a known operational change. If correlated indicators persist, apply a phased review to the affected component before extending a restriction to the entire service.

Limits, additional reviews, or pauses should have a defined scope, an owner, and a reassessment criterion. There is no single threshold that is valid for every service. If a pause may affect users, prepare a recovery alternative that is compatible with your service rules and applicable regulations.

• Validate the data first and confirm the scope of the anomaly.
• Where technically feasible, prioritize targeted and reversible controls.
• Set a duration or review condition for any pause; avoid indefinite blocks without reassessment.
• Escalate to the relevant operations, security, and provider teams when the evidence or impact warrants it.

Reduce false positives and enable recovery

Effective protection does not mean treating every unusual request as malicious. Check the event against product context, recent changes, and aggregate service behavior. Before expanding a rule, review whether it disproportionately affects certain users or destinations.

Provide a recovery path for anyone unable to receive an OTP, in line with the options your service actually supports. Explain the next step without revealing details that could facilitate abuse. Adjust or remove a measure when the evidence no longer justifies it, and record who authorized the change.

• Separate the decision to investigate from the decision to block.
• Check the impact on legitimate users before extending a measure.
• Establish a review and recovery channel appropriate to the service.
• Reassess rules using documented confirmed incidents and false positives.

Investigation procedure and evidence log

Open a case with the alert, time window, systems involved, and reason for review. Preserve the data needed to reconstruct the sequence and note its sources, transformations, and limitations. Avoid collecting personal information that is unnecessary for the investigation; follow relevant access and retention policies and applicable legal obligations.

State conclusions in terms of certainty: observed anomaly, correlated indicators, pending hypothesis, or sufficient evidence under internal criteria. Record the actions taken, their scope, the owner, the time, and the outcome of reassessment. Escalate cases that exceed the team’s authority or require provider validation.

• Alert and scope: what changed, when, and which flow is affected.
• Evidence: events and sources reviewed, along with any limitations identified.
• Decision: action chosen, alternatives considered, and rationale.
• Follow-up: owner, review deadline or condition, and outcome.

Checklist for reviewing an alert

Before closing or escalating the case, confirm that you can explain which data supports the alert and what remains uncertain. The goal is to improve the operational decision, not to automatically label people or expose sensitive information.

Use this checklist as a review framework and adapt it to your systems’ actual capabilities, provider agreements, and applicable obligations.

• Has it been confirmed that the increase is not due to a logging error or a known operational change?
• Were volume, destination distribution, request behavior, and costs compared across relevant periods?
• Were application events, submission attempts, and reported statuses distinguished?
• Was a DLR or HLR lookup avoided as conclusive proof of fraud or receipt?
• Is the chosen measure proportionate, scoped, and subject to reassessment?
• Is there a recovery path for legitimate users, and are retained personal data minimized?
• Were the evidence, uncertainties, decision, and follow-up documented?