| EX-0010 |
Malicious Code |
The adversary achieves on-board effects by introducing executable logic that runs on the vehicle, either native binaries and scripts, injected shellcode, or “data payloads” that an interpreter treats as code (e.g., procedure languages, table-driven automations). Delivery commonly piggybacks on legitimate pathways: software/firmware updates, file transfer services, table loaders, maintenance consoles, or command sequences that write to executable regions. Once staged, activation can be explicit (a specific command, mode change, or file open), environmental (time/geometry triggers), or accidental, where operator actions or routine autonomy invoke the implanted logic. Malicious code can target any layer it can reach: altering flight software behavior, manipulating payload controllers, patching boot or device firmware, or installing hooks in drivers and gateways that bridge bus and payload traffic. Effects range from subtle logic changes (quiet data tampering, command filtering) to overt actions (forced mode transitions, resource starvation), and may include secondary capabilities like covert communications, key material harvesting, or persistence across resets by rewriting images or configuration entries. |
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EX-0010.01 |
Ransomware |
Ransomware on a spacecraft encrypts data or critical configuration so that nominal operations can no longer proceed without the attacker’s cooperation. Targets include mass-memory file stores (engineering telemetry, payload data), configuration and command tables, event logs, on-board ephemerides, and even intermediate buffers used by downlink pipelines. Some variants interfere with key services instead of bulk data, e.g., encrypting a command dictionary or table index so valid inputs are rejected, or wrapping the payload data path in an attacker-chosen cipher so downlinked products appear as noise. By denying access to on-board content or control artifacts at scale, attackers convert execution into bargaining power or irreversible mission degradation. |
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EX-0010.02 |
Wiper Malware |
Wipers deliberately destroy or irreversibly corrupt data and, in some cases, executable images to impair or end mission operations. Destructive routines may overwrite with patterns or pseudorandom data, repeatedly reformat volumes, trigger wear mechanisms on non-volatile memory, or manipulate low-level translation layers so recovery tools see a blank or inconsistent device. Activation can be immediate or staged, sleeping until a specific time, pass, or maintenance action, and may be paired with anti-recovery steps such as erasing checksums, undo logs, or golden images. Because wipers operate at storage and image layers that underpin many subsystems, collateral effects can cascade: autonomy enters safing without viable recovery paths, downlinks carry only noise, and subsequent updates cannot be authenticated or applied. The defining feature is irreversible loss of data or executables as the primary objective, rather than concealment or monetization. |
| EX-0012 |
Modify On-Board Values |
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. |
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EX-0012.13 |
Poison AI/ML Training Data |
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. |
| DE-0003 |
On-Board Values Obfuscation |
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. |
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DE-0003.12 |
Poison AI/ML Training for Evasion |
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. |
| DE-0010 |
Overflow Audit Log |
The adversary hides activity by exhausting finite on-board logging and telemetry buffers so incriminating events are overwritten before they can be downlinked. Spacecraft typically use ring buffers with severity filters, per-subsystem quotas, and scheduled dump windows; by generating bursts of benign but high-frequency events (file listings, status queries, low-severity housekeeping, repeated mode toggles) or by provoking chatter from chatty subsystems, the attacker accelerates rollover. Variants target recorder indexes and event catalogs so new entries displace older ones, or they align floods with known downlink gaps and pass handovers when retention is shortest. To analysts on the ground, logs appear present but incomplete, showing a plausible narrative that omits the very interval when unauthorized commands or updates occurred. |