Why logging and monitoring are part of the attack surface

The OWASP API Security Top 10 introduced a dedicated entry in the 2019 edition, API10:2019 Insufficient Logging & Monitoring. OWASP describes the risk succinctly: without logging and monitoring, or with insufficient logging and monitoring, it is nearly impossible to track suspicious activity and respond to it in time. Attackers exploit exactly this gap to abuse systems undetected, gaining time to compromise them fully.

In the 2023 edition, this point wasn't removed but folded into API8:2023 Security Misconfiguration. There, OWASP explicitly lists incorrectly set or missing logging features among the vulnerabilities. Logging is therefore not a downstream operations topic, but part of secure configuration for the entire API stack.

OWASP names two typical scenarios for insufficient logging: leaked access keys for an admin API, where the organization cannot reconstruct what data was exfiltrated because of missing logs, and a credential stuffing attack where failed logins were written but never evaluated, so no alert was triggered. Both show the core problem: writing logs isn't enough, they must also be continuously monitored.

PatternVisible Behavior in the LogExample Event (OWASP Vocabulary)
BOLA probing / object enumerationSequential iteration through object IDs, many authorization failuresauthz_fail, malicious_direct_reference
Scraping / data harvestingHigh, steady volume across list and detail endpointsexcess_rate_limit_exceeded
Credential stuffingMany failed logins across many accounts, distributed sourcesauthn_login_fail, authn_login_fail_max
Account takeoverLogin from implausible geolocation, token reuseauthn_impossible_travel, authn_token_reuse
Endpoint / inventory scanClusters of 404s and access to unspecified pathsmalicious_excess_404

Abuse patterns and their log indicators

What must be logged

OWASP specifies a clear minimum set for API logging. All failed authentication attempts, denied access (authorization failures), and input validation errors should be logged. The OWASP Logging Cheat Sheet adds to this list the events that are security-relevant in an API:

  • Authentication: successful and failed logins, lockouts, password and token changes. Clusters of failed attempts for the same account, from many IP addresses, or in rapid bursts are early indicators of brute force, credential stuffing, and password spraying.
  • Authorization: access control violations, i.e. requests for objects or functions the caller is not entitled to. Such events are the logging correlate of BOLA and BFLA.
  • Input and output validation: protocol violations, invalid parameters, schema violations. According to OWASP, a failure against a finite set of values is a high-severity security event, because it can only arise from attack activity.
  • Session and token events: suspicious JWT validation failures, tampered session identifiers, token reuse.
  • Higher-privileged functions: user management, permission changes, administrative actions, as well as starting, stopping, and configuration changes of services.

At the transport and status level, above all the 4xx and 5xx responses belong in monitoring. An unusual clustering of 401/403 points to permission probing, many 404s to enumeration or inventory scans, and a rise in 5xx to faulty or overloading requests. OWASP also points out that there is no universal checklist: scope and level of detail should be guided by security risk, otherwise alert noise arises in which real incidents get lost.

What must never go into the log

Logs are themselves a rewarding attack target and are subject to data protection law. OWASP therefore treats logs as sensitive data whose integrity must be protected at rest and in transit. According to the OWASP Logging Cheat Sheet, certain content must not appear at all, or only masked, hashed, or encrypted:

  • Authentication passwords and access tokens as well as session identifiers (replace with a hash value if needed).
  • Encryption keys and other secrets, database connection strings.
  • Banking and payment card data, as well as sensitive personal data (e.g. health or identity document data).
  • Application source code and data of a higher protection class than the logging system is permitted to store.
  • Data whose collection is unlawful in the respective jurisdiction, or for which no consent exists.

Even less sensitive fields such as IP addresses, internal network names, file paths, or contact details must be handled with care. The OWASP Logging Vocabulary Cheat Sheet frames this as the responsibility of the data steward: an IP address can be useful for detection but can become personally identifiable in combination with other fields. This aligns with the principle of data minimization. A common, avoidable mistake is logging complete request headers or bodies that contain Authorization headers or tokens.

Structured logs, correlation, and SIEM

For logs to be machine-analyzable, OWASP requires a format that a log management solution can consume, with enough detail to identify the actor. Every entry should capture the who, where, when, and what: timestamp, application and host identifier, source address and user identity (if known), as well as the type and severity of the event.

The OWASP Logging Vocabulary Cheat Sheet proposes a unified JSON schema and standardized event naming for this, such as authn_login_fail, authn_login_fail_max, authz_fail, excess_rate_limit_exceeded, or malicious_excess_404. Timestamps should be in ISO 8601 format with a UTC offset, so that entries are comparable across systems. An entry might look like this:

  • { "datetime": "2026-01-01T01:01:01-0700", "appid": "foobar.netportal_auth", "event": "authn_login_success:joebob1", "level": "INFO", "source_ip": "165.225.50.94", "request_uri": "/api/v2/auth/", "request_method": "POST" }

For severity classification, RFC 5424 (Syslog) establishes the well-known levels from Emergency to Debug. A consistent correlation ID per request makes it possible to trace a call across gateway, microservices, and downstream services, which is essential in distributed architectures.

OWASP recommends consolidating all logs from the API stack and hosts into a SIEM (Security Information and Event Management) and equipping it with dashboards and alerts, so suspicious activity is detected and responded to sooner. NIST SP 800-92 (Guide to Computer Security Log Management) provides the organizational framework for this: central log management infrastructure, defined retention, and repeatable evaluation processes. Further automation is picked up by SOAR (Security Orchestration, Automation and Response), which responds to detected patterns with defined playbooks, such as a token revocation or a temporary block.

Anomaly detection: enumeration, scraping, and BOLA patterns

Many API attacks consist of formally valid individual requests that only become conspicuous as a pattern. A BOLA exploit, for example, works by an attacker iterating through object identifiers such as user or order IDs. Each individual request can be syntactically correct and authenticated. What becomes conspicuous is the behavior over time: sequential incrementing of IDs, a high proportion of accesses to other users' objects, many 403 responses, or an unusually high number of distinct objects per session.

Typical abuse patterns and their indicators:

  • Object enumeration / BOLA probing: monotonically increasing or randomly tried identifiers, many authorization failures. Vendor tools such as Cloudflare API Shield detect BOLA attempts partly by enumeration over object IDs.
  • Scraping / data harvesting: high, steady request volume across listing and detail endpoints, often with a conspicuous user agent or a wide spread of source IPs.
  • Credential stuffing / account takeover: many failed logins for different accounts from few addresses or distributed, unusual geolocation (authn_impossible_travel).
  • Endpoint and inventory scans: clusters of 404s and requests to unspecified paths (malicious_excess_404).

Detection builds on two levels: thresholds and rates (e.g. requests per token, 4xx rate per endpoint) and behavioral baselines that learn a client's normal access pattern and flag deviations. Both require a structured log stream correctly attributed per endpoint and consumer. It's important to draw the line: anomaly detection uncovers abuse but does not replace server-side authorization checks, which prevent BOLA at the root.

Audit trail, traceability, and the link to Improper Inventory

Logging serves two related purposes: near-real-time detection and a solid audit trail for later investigation. OWASP emphasizes that the integrity of logs must be protected, among other things against log injection, so that records remain admissible in court and in forensics. In the German context, the BSI IT-Grundschutz module DER.1 Detektion von sicherheitsrelevanten Ereignissen describes how information is collected, correlated, and evaluated to detect events as completely and promptly as possible. According to BSI, this requires comprehensive logging, which is governed by the module OPS.1.1.5 Protokollierung.

There is a close interplay with Improper Inventory Management (API9:2023): what isn't in the API inventory usually isn't monitored either. Forgotten, undocumented, or outdated endpoints (shadow and zombie APIs) either produce no logs, or logs that no one evaluates, becoming a preferred target. Conversely, monitoring real traffic uncovers endpoints that appear in no specification. Complete inventory and complete logging depend on each other.

This is exactly where Venedy's claim to traceability comes in: provable findings instead of a black box. Findings on an interface can be traced back to the underlying observations, so security and compliance stakeholders learn not just that a risk exists, but why. This complements the logging and monitoring practices described here, but doesn't replace them.

Sources

Keep reading
API9Improper Inventory Management API8Security Misconfiguration FundamentalsAPIs in Production