Protect the [Selection (one or more): confidentiality; integrity] of the following information at rest: [Assignment: organization-defined information at rest].
Protection of data at rest spans on-board stores (flight images, keys, logs, payload data) and ground repositories. Consider verified boot and integrity checks for images and configuration tables, encryption where feasible, and access controls aligned to mission roles and enclaves. Plan for forensic preservation under limited downlink by prioritizing what is retained during outages, and use health/ECC indicators so operators can gauge data viability after radiation events.
When artificial intelligence (AI) or machine learning (ML) is employed for mission-critical spacecraft or ground system operations, the integrity of the training data set is a foundational security requirement, not merely a data quality concern. Data poisoning attacks introduce or modify training samples, labels, or other data-pipeline inputs to alter resulting model behavior. Poisoning may cause general performance degradation, targeted misclassification, or backdoor behavior that appears only when specific operational inputs or triggers are present. Remediation may require identifying and removing affected data, retraining or fine-tuning from a trusted dataset, or rolling back to a validated model. Because remediation can be difficult and may not identify every affected behavior, controls should prioritize preventing unauthorized data changes and detecting suspicious inputs before training while maintaining recoverable trusted dataset and model versions. Detection and prevention techniques include validity checking of data sets to identify statistically anomalous or out-of-distribution inputs, statistical analysis to detect injected samples that deviate from expected data distributions, manual analysis of flagged inputs, and regression testing over time to identify model behavioral drift that may indicate successful historical poisoning. These controls must be applied as part of a continuous data governance process spanning the full training pipeline, from data collection and curation through model validation and operational deployment.
* The intent as written is for all transmitted traffic to be protected. This includes internal to internal communications and especially outside of the boundary.
SPR-126
The [spacecraft] shall protect the confidentiality and integrity of the [all information] using cryptography while it is at rest.{SV-IT-2,SV-CF-2}{SC-28,SC-28(1),SI-7(6)}
* Information at rest refers to the state of information when it is located on storage devices as specific components of information systems. This is often referred to as data-at-rest encryption.
SPR-462
The [spacecraft] shall support delegation of temporary data storage to [organization]-authorized alternate nodes or spacecraft and shall preserve confidentiality, integrity, and access controls for the delegated data.{SV-CF-1,SV-CF-2,SV-AC-1}{CP-2(6),SC-28,AC-3}
Delegated storage or processing expands trust boundaries. Maintaining CIA protections during delegation prevents exposure. Secure federation supports constellation-based architectures. Controlled delegation strengthens distributed resilience.
The attacker alters live or persistent data that the spacecraft uses to make decisions and route work. Targets include device and control registers, parameter and limit tables, internal routing/subscriber maps, schedules and timelines, priority/QoS settings, watchdog and timer values, autonomy/FDIR rule tables, ephemeris and attitude references, and power/thermal setpoints. Many missions expose legitimate mechanisms for updating these artifacts, direct memory read/write commands, table load services, file transfers, or maintenance procedures, which can be invoked to steer behavior without changing code. Edits may be transient (until reset) or latched/persistent across boots; they can be narrowly scoped (a single bit flip on an enable mask) or systemic (rewriting a routing table so commands are misdelivered). The effect space spans subtle biasing of control loops, selective blackholing of commands or telemetry, rescheduling of operations, and wholesale changes to mode logic, all accomplished by modifying the values the software already trusts and consumes.
When missions employ AI/ML, for onboard detection/classification, compression, anomaly screening, guidance aids, or ground-side planning, training data becomes a control surface. Data poisoning inserts crafted examples or labels into the training corpus or fine-tuning set so the resulting model behaves incorrectly while appearing valid. Variants include clean-label backdoors (benign-looking samples with a hidden trigger that later induces a targeted response), label flipping and biased sampling (to skew decision boundaries), and corruption of calibration/ground-truth products that the pipeline trusts. For space systems, poisoning may occur in science archives, test vectors, simulated scenes, or housekeeping datasets used to train autonomy/anomaly models; models trained on poisoned corpora are then packaged and uplinked as routine updates. Once fielded, a simple trigger pattern in imagery, telemetry, or RF features can cause misclassification, suppression, or false positives at the time and place the adversary chooses, turning model behavior into an execution mechanism keyed by data rather than code.
The adversary manipulates housekeeping and control values that operators and autonomy rely on to judge activity, health, and command hygiene. Targets include command/telemetry counters, event/severity flags, downlink/reporting modes, cryptographic-mode indicators, and the system clock. By rewriting, freezing, or biasing these fields, and by selecting reduced or summary telemetry modes, unauthorized actions can proceed while the downlinked picture appears routine or incomplete. The result is delayed recognition, misattribution to environmental effects, or logs that cannot be reconciled post-facto.
When security monitoring relies on AI/ML (e.g., anomaly detection on telemetry, RF fingerprints, or command semantics), the training data itself is a target. Data-poisoning introduces crafted examples or labels so the learned model embeds false associations, treating attacker behaviors as normal, or flagging benign patterns instead. Variants include clean-label backdoors keyed to subtle triggers, label flipping that shifts decision boundaries, and biased sampling that suppresses rare-but-critical signatures. Models trained on tainted corpora are later deployed as routine updates; once in service, the adversary presents inputs containing the trigger or profile they primed, and the detector omits or downranks the very behaviors that would reveal the intrusion.