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T1565.001 is stored data manipulation: inserting, deleting, or replacing data at rest so a later process produces an attacker-chosen result. In cloud machine-learning pipelines, a replaced model file can become code execution when a serving runtime deserialises it. DCV maps GuardDuty's Impact:S3/MaliciousIPCaller and builds the broader detection strategy around PutObject, CopyObject, and ModifyDBInstance events. Model pipelines need the same object-generation telemetry, bucket-ownership checks, and release manifests before import, followed by deployment correlation.
Adversaries may insert, delete, or manipulate data at rest in order to influence external outcomes or hide activity, thus threatening the integrity of the data. By manipulating stored data, adversaries may attempt to affect a business process, organizational understanding, and decision making.
Stored data could include a variety of file formats, such as Office files, databases, stored emails, and custom file formats. The type of modification and the impact it will have depends on the type of data as well as the goals and objectives of the adversary. For complex systems, an adversary would likely need special expertise and possibly access to specialized software related to the system that would typically be gained through a prolonged information gathering campaign in order to have the desired impact.
Platforms: Linux, macOS, Windows.
DCV does not currently ship a cloud-audit-log finding mapped directly to T1565.001. The technique earns a library page because a13e research cites it. Detection sits downstream, on the exploitation step the technique enables.
CloudSigma does not currently ship a stand-alone rule that fires on T1565.001 in isolation. Generate a starting-point rule from the CVE, vulnerability disclosure, or threat-research blog post that exercises this technique, then pair it with SIEM-side correlation before enabling in production.
High-fidelity detection of T1565.001 requires correlation
across multiple events. For example, a credential-validation call
followed by a reconnaissance chain (List* /
Describe*) within a short window from an unfamiliar
source. A single-event Sigma rule on
GetCallerIdentity alone fires constantly on
legitimate CLI, SDK and CI/CD activity.
Where you have a specific advisory, vulnerability disclosure or blog post that exercises T1565.001-style abuse, CloudSigma can generate a starting-point rule from that input. You then deploy it in your SIEM and combine it with the SIEM's native correlation features (timeframe joins across users, source-IP anomalies, impossible-travel checks). For T1565.001 specifically the generated rule is rarely sufficient on its own; pair it with the SIEM-side correlation logic before enabling in production.
T1565.001 is stored data manipulation: inserting, deleting, or replacing data at rest so a later process produces an attacker-chosen result. In cloud machine-learning pipelines, a replaced model file can become code execution when a serving runtime deserialises it. DCV maps GuardDuty's Impact:S3/MaliciousIPCaller and builds the broader detection strategy around PutObject, CopyObject, and ModifyDBInstance events. Model pipelines need the same object-generation telemetry, bucket-ownership checks, and release manifests before import, followed by deployment correlation.
T1565.001 has no cloud-audit-log signal of its own; DCV does not currently ship a finding mapped directly to it. The technique earns a library page because a13e research cites it. Detection sits downstream, on the exploitation step the technique enables (see Related techniques).
T1565.001 is part of MITRE ATT&CK TA0040 Impact: How adversaries disrupt or destroy systems and data.
T1565.001 requires multi-event correlation that exceeds a single Sigma rule's structure. CloudSigma can generate a starting-point rule from a CVE, vulnerability disclosure, or threat-research blog post that exercises T1565.001-style abuse; pair it with SIEM-side correlation logic before enabling in production.