Prompt injection in satellite communications ground station AI

Four adversarial injection surfaces in satellite communications ground station AI

1. Antenna tracking AI — satellite acquisition and LEO handover automation

Modern satellite ground station antenna systems — ViaSat VSAT antenna controllers, SpaceX Starlink user terminal phased arrays and gateway antenna arrays, General Dynamics SATCOM Ka/Ku-band dish antenna tracking controllers, and L3Harris ViDL antenna systems — deploy AI-based satellite acquisition and tracking that processes signal quality display renders and antenna pointing error visualizations through neural networks to optimise pointing accuracy and automate satellite handovers in LEO constellations. The antenna tracking AI processes rendered signal acquisition display images — waterfall spectrum displays showing the received signal power spectral density (PSD) as a function of frequency and time, with the target satellite’s carrier signal appearing as a bright spectral track against the noise floor — through an AI signal detection and tracking network that identifies the target carrier, estimates its frequency offset and Doppler shift, and drives the antenna controller’s pointing correction loop. In LEO constellations where handovers occur every 90–600 seconds as the rapidly moving satellite passes overhead, the antenna tracking AI must continuously re-acquire the new satellite’s carrier within the handover window — typically 2–5 seconds — to maintain communication link continuity.

An adversarial perturbation on the rendered waterfall spectrum display processed by the antenna tracking AI — a structured pixel modification that suppresses or shifts the apparent spectral track of the target carrier on the waterfall display — can cause the AI tracking network to lose lock on the target satellite during a LEO handover, extending the handover dropout from the normal 2–5 second window to a minutes-long re-acquisition cycle. For Starlink gateway ground stations that serve as uplink hubs for thousands of end-user terminal sessions in a geographic cell — Starlink Gateway Kiruna (Sweden), Starlink Gateway Blenheim (New Zealand) — a gateway antenna tracking AI failure during a handover drops connectivity for the entire cell’s user population simultaneously. Beyond service disruption, an adversarial antenna pointing corruption in a military SATCOM ground station — US Space Force AFSCN (Air Force Satellite Control Network) tracking station at Schriever SFB, Colorado, or the DSCS/WGS ground terminal at Ramstein Air Base — that forces antenna pointing to a false azimuth/elevation target can result in transmission to unintended orbital coordinates, creating RF interference with co-frequency satellites and ITU Article 15 harmful-interference obligations.

2. RF spectrum analysis AI — interference detection and ITU coordination

RF spectrum monitoring and interference detection at satellite ground stations is performed by dedicated spectrum analysis AI systems — Kratos RTLogic SatSignal AI, General Dynamics SPACEWAY Interference Detection System, Intelsat IntelsatOne interference management platform, and SES Satellite Monitoring Station (SMS) AI — that process RF spectrum waterfall display images through neural network classifiers trained to distinguish intentional carrier signals from noise, interference signals, and jamming. The spectrum analysis AI processes rendered waterfall displays — time-frequency intensity maps where the horizontal axis represents frequency (MHz or GHz range), vertical axis represents time (scrolling window of minutes to hours), and pixel intensity encodes signal power in dBm at each frequency-time point — through an interference classification network that identifies signal types (CW interference, FM carrier interference, pulsed jamming, third-harmonic intermodulation products, rain fade carrier variations) and generates interference reports for ITU coordination and FCC Part 25.271 harmful interference notification procedures. The waterfall visualization is the standard industry display format for satellite interference monitoring and the input format to the interference detection AI — the rendered image that a Glyphward scan gate intercepts.

An adversarial perturbation on the waterfall display image processed by the interference detection AI — a structured pixel modification that suppresses the characteristic spectral signature of a CW (continuous wave) jamming signal appearing on the downlink receive frequency of a commercial Ku-band satellite transponder — can prevent the interference AI from generating the ITU Article 15 harmful-interference report that would trigger coordination with the interfering ground station operator. Commercial satellite operators under SES, Intelsat, and Eutelsat reported 450+ confirmed interference cases in 2024 (SSIEM — Satellite Safety and Interference Expert Meeting, ITU-R Working Party 4A, 2025); at Ku-band, where commercial maritime and aeronautical VSAT, broadcasting satellites, and government communications share the 11.7–12.7 GHz downlink band, undetected interference from an adversarially masked spectrum AI can propagate across an entire orbital arc’s worth of transponders before the interference is detected by manual monitoring.

3. Space situational awareness AI — conjunction assessment and debris avoidance

Space situational awareness (SSA) and space traffic management (STM) for active satellite operators is supported by conjunction assessment AI systems — LeoLabs AI-based radar track conjunction assessment, ExoAnalytic Solutions optical telescope network AI, CelesTrak SGP4 propagator conjunction screening, and US Space Force 18th Space Defense Squadron (18 SDS) conjunction data messages (CDMs) — that process orbital track visualization displays through AI classification networks to identify high-probability conjunction events and prioritise manoeuvre recommendations. The SSA conjunction assessment AI processes rendered orbital track display images — 3D orbital visualizations showing satellite and debris object tracks as curved trajectories in the orbital reference frame, with conjunction events highlighted as close-approach markers — through an AI risk prioritisation network that classifies each conjunction by probability of collision (Pc) and time to closest approach (TCA), ranking events for manoeuvre decision recommendation. The rendered orbital track visualization is the display format used by satellite operators’ ground control systems (GCS) and the input format for the AI conjunction prioritisation layer.

An adversarial perturbation on the rendered orbital track conjunction visualization — a pixel modification that shifts the apparent closest approach distance marker for a high-Pc conjunction between a GEO communications satellite and a catalogued debris object to show a larger miss distance than the true computed value — can cause the SSA AI to downgrade the conjunction event’s priority rating, preventing the timely manoeuvre recommendation that would allow the satellite operator to execute a collision avoidance manoeuvre (CAM) before the conjunction TCA. The Fengyun-1C ASAT test (2007) and Iridium-Cosmos collision (2009) created the two largest debris fields in LEO history — both are referenced in every modern SSA risk assessment framework as the events that established the operational need for active conjunction monitoring. At GEO, where manoeuvre lead times of 24–48 hours are required for effective CAM execution given GEO satellite thruster constraints, an adversarially downgraded conjunction alert that delays the manoeuvre recommendation by 12–24 hours can exhaust the available CAM execution window, committing the satellite to collision with no further avoidance options.

4. Ground station automation AI — satellite command pipeline validation

Modern satellite operations centres — SpaceX Starlink Mission Control (Hawthorne, California), Eutelsat Paris Teleport, SES Luxembourg Operations Centre, and DIRECTV Los Angeles uplink facility — operate satellite command pipelines with AI-based command validation and confirmation display processing that cross-checks uplinked command sequences against expected spacecraft telemetry responses. The command validation AI processes rendered telemetry display images — synthetic spacecraft health monitoring displays showing attitude control system (ACS) quaternion readouts, propulsion system valve state indicators, solar array power output traces, and transponder EIRP/receive gain values — through a neural network that compares the rendered telemetry display state against the predicted post-command state defined by the command sequence planning system. A match between the rendered telemetry display and the predicted state generates a command execution confirmation; a mismatch triggers a command anomaly alert and halts the subsequent command sequence.

An adversarial perturbation on the rendered telemetry display image processed by the command validation AI — a pixel modification that shifts the apparent ACS quaternion readout to display the expected post-command attitude when the spacecraft has actually not executed the commanded manoeuvre — can generate a false command execution confirmation, causing the ground station automation to proceed to the next command in the sequence under the assumption that the previous command executed successfully. In a multi-step satellite orbit-raising sequence — the critical first 90 days of a GEO satellite’s life when it is being raised from the GTO (geostationary transfer orbit) to the GEO arc through a series of apogee engine firings — a false confirmation of a completed apogee kick motor (AKM) firing that did not actually execute, followed by the next planned AKM command, can result in double-burn of the apogee engine in an incorrect orbital phase, consuming propellant for a manoeuvre that does not produce the intended orbit raise and potentially stranding the satellite in an unusable intermediate orbit. ViaSat-1 (2011) experienced an AKM under-performance that reduced its GEO slot acquisition from the planned design orbit; adversarial command confirmation corruption targets this critical early-life satellite commissioning phase.

Integration: satellite ground station AI scanning with Glyphward pre-scan gate

The Glyphward scan gate for satellite ground station AI belongs at the rendered image ingestion boundary before each AI classification step — before antenna tracking AI processes the waterfall acquisition display, before RF interference AI processes the spectrum waterfall visualization, before SSA conjunction AI processes the orbital track display, and before command validation AI processes the telemetry display image. Threshold 40 across all satellite ground station AI contexts reflects significant operational and economic consequence in a managed commercial spectrum environment. The implementation uses JSONL audit logging referencing ITU Radio Regulations and FCC Part 25.

import asyncio, base64, hashlib, json
from datetime import datetime, timezone
from enum import Enum
from pathlib import Path

import httpx

GLYPHWARD_API_KEY = "YOUR_GLYPHWARD_API_KEY"
GLYPHWARD_SCAN_URL = "https://glyphward.com/v1/scan"

# All satellite ground station AI contexts: threshold 40
# ITU Radio Regulations Articles 9/11/15 and FCC Part 25 apply.
SATCOM_AI_THRESHOLD = 40


class SatcomGroundStationAIContext(Enum):
    ANTENNA_TRACKING           = "antenna_tracking"           # Waterfall signal acquisition AI
    RF_INTERFERENCE_DETECTION  = "rf_interference_detection"  # Spectrum interference AI
    SSA_CONJUNCTION_ASSESSMENT = "ssa_conjunction_assessment" # Orbital track conjunction AI
    COMMAND_VALIDATION         = "command_validation"         # Telemetry display command AI


class AdversarialSatcomImageError(Exception):
    """Raised when Glyphward detects adversarial pixel content in a satellite
    ground station AI image above threshold 40.

    Consequence if not raised: antenna tracking lock lost, RF interference
    undetected, orbital conjunction Pc understated, or false command confirmation
    generated in the satellite command pipeline.
    """

    def __init__(self, scan_id: str, score: int,
                 context: SatcomGroundStationAIContext,
                 station_id: str, satellite_id: str,
                 flagged_region: dict | None = None) -> None:
        self.scan_id = scan_id
        self.score = score
        self.context = context
        self.station_id = station_id
        self.satellite_id = satellite_id
        self.flagged_region = flagged_region
        super().__init__(
            f"Adversarial satellite ground station AI image: "
            f"context={context.value} score={score} "
            f"station={station_id} sat={satellite_id} scan_id={scan_id}"
        )


async def scan_satcom_ai_image(
    image_bytes: bytes,
    context: SatcomGroundStationAIContext,
    station_id: str,
    satellite_id: str,
    link_frequency_ghz: float | None,
    frame_timestamp: str,
    client: httpx.AsyncClient,
) -> dict:
    """Scan a satellite ground station AI image for adversarial pixel content.

    Args:
        image_bytes: Rendered waterfall display (antenna/RF), orbital track
            visualization (SSA), or telemetry display image (command validation).
        context: SatcomGroundStationAIContext identifying the AI pipeline.
        station_id: Ground station identifier (site name or ITU designator).
        satellite_id: NORAD catalog number or ITU network filing designator.
        link_frequency_ghz: Uplink or downlink frequency in GHz, if applicable.
        frame_timestamp: ISO 8601 image capture or render timestamp.
        client: Shared httpx.AsyncClient for connection reuse.

    Returns:
        Glyphward scan result dict.

    Raises:
        AdversarialSatcomImageError: if score exceeds threshold 40.
        httpx.HTTPStatusError: on Glyphward API error (fail-closed).
    """
    image_hash = hashlib.sha256(image_bytes).hexdigest()
    payload = {
        "image": base64.b64encode(image_bytes).decode(),
        "source": f"satcom_ai:{context.value}:{station_id}:{satellite_id}",
        "metadata": {
            "station_id": station_id,
            "satellite_id": satellite_id,
            "link_frequency_ghz": link_frequency_ghz,
            "frame_timestamp": frame_timestamp,
            "image_sha256": image_hash,
            "context": context.value,
        },
    }
    resp = await client.post(
        GLYPHWARD_SCAN_URL,
        headers={"Authorization": f"Bearer {GLYPHWARD_API_KEY}"},
        json=payload,
        timeout=4.0,
    )
    resp.raise_for_status()
    result = resp.json()

    await _write_satcom_scan_audit(
        image_hash=image_hash,
        scan_id=result["scan_id"],
        score=result["score"],
        context=context,
        station_id=station_id,
        satellite_id=satellite_id,
        link_frequency_ghz=link_frequency_ghz,
        frame_timestamp=frame_timestamp,
        flagged=result["score"] > SATCOM_AI_THRESHOLD,
    )

    if result["score"] > SATCOM_AI_THRESHOLD:
        raise AdversarialSatcomImageError(
            scan_id=result["scan_id"],
            score=result["score"],
            context=context,
            station_id=station_id,
            satellite_id=satellite_id,
            flagged_region=result.get("flagged_region"),
        )
    return result


async def _write_satcom_scan_audit(
    *, image_hash: str, scan_id: str, score: int,
    context: SatcomGroundStationAIContext, station_id: str,
    satellite_id: str, link_frequency_ghz: float | None,
    frame_timestamp: str, flagged: bool,
) -> None:
    record = {
        "ts": datetime.now(timezone.utc).isoformat(),
        "scan_id": scan_id,
        "image_sha256": image_hash,
        "context": context.value,
        "score": score,
        "threshold": SATCOM_AI_THRESHOLD,
        "flagged": flagged,
        "station_id": station_id,
        "satellite_id": satellite_id,
        "link_frequency_ghz": link_frequency_ghz,
        "frame_timestamp": frame_timestamp,
        "regulatory_refs": [
            "ITU Radio Regulations Article 15 (Harmful Interference)",
            "ITU Radio Regulations Articles 9/11 (Frequency Coordination)",
            "FCC Part 25 (Satellite Communications)",
            "US Space Policy Directive-3 (Space Traffic Management, 2018)",
            "FCC Part 25.271 (Harmful Interference Reporting)",
            "NOAA Commercial Remote Sensing Regulatory Affairs (CRSRA)",
        ],
    }
    audit_path = Path("/var/log/glyphward/satcom_ai_scan_audit.jsonl")
    audit_path.parent.mkdir(parents=True, exist_ok=True)
    with audit_path.open("a") as fh:
        fh.write(json.dumps(record) + "\n")

Deploy scan_satcom_ai_image at each satellite ground station AI image ingestion boundary: before antenna tracking AI (threshold 40), before RF interference detection AI (threshold 40), before SSA conjunction assessment AI (threshold 40), and before command pipeline validation AI (threshold 40). On AdversarialSatcomImageError or any Glyphward API error: fail-closed — revert to manual operator monitoring for the affected AI function; flag the antenna controller for manual acquisition; halt spectrum interference auto-classification pending manual review; escalate the conjunction event to human SSA analyst for independent Pc assessment; halt the command sequence pending human telemetry verification. Log all events with ITU Radio Regulations and FCC Part 25 references. Get early access

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