Prompt injection in counter-UAS and anti-drone AI

Four adversarial injection surfaces in counter-UAS AI

1. Radar-fused EO/IR drone classification AI (Dedrone, Fortem, DroneShield)

Commercial C-UAS platforms use a multi-layer detection architecture where radar provides initial detection (range, azimuth, altitude, track velocity) and the EO/IR camera provides classification — the neural network that answers the question “is this a DJI Mavic 3, a Cooper’s Hawk, or a Cessna 172?” The Dedrone DroneTracker platform (deployed at San Francisco International Airport SFO, Sacramento International Airport SMF, and the 2024 Paris Olympics) uses a YOLO-based drone classification network trained on a corpus of rendered EO/IR camera frames of DJI, Autel, Skydio, and artisanal FPV racing drone models, plus bird silhouette and slow fixed-wing aircraft classifier classes. Fortem Technologies’ TrueView radar with DragonFire hunter drone uses the same EO/IR classification architecture — the TrueView radar cues a PTZ camera to the detected track, and a DragonFire classification AI determines drone species before the hunter drone is dispatched for kinetic net-gun interdiction. The classification AI output drives the response decision tree: RF jamming (authorized federal facility use only under 18 USC 32 and 47 USC 333 anti-jamming statute), kinetic interdiction (hunter drone, net gun), or alert-only dispatch for subsequent visual confirmation. An adversarial perturbation on the EO/IR camera frame processed by the classification AI — a pixel modification that shifts the apparent silhouette, thermal signature, or propeller motion blur of a DJI Mavic 3 toward the bird silhouette class — causes the AI to classify the detected track as a non-drone object, suppressing the response decision and allowing the drone to continue toward the protected asset without interdiction.

The adversarial attack on C-UAS classification AI is not hypothetical: Zhao et al. (2019, “Seeing isn’t believing: Towards More Robust Adversarial Attack Against Real World Object Detectors”, ACM CCS) demonstrated that YOLO-based drone detection networks are vulnerable to physical adversarial patches — printed patterns attached to drone fuselages that suppress detection at operational ranges. University of Waterloo research (2022) demonstrated adversarial camouflage textures on DJI Phantom 4 Pro airframes that reduced YOLOv5 drone detection accuracy from 97.8% to 23.4% at 30m range. Physical adversarial patches are a form of adversarial pixel injection visible to the AI at frame-inference time; the C-UAS camera AI processes the rendered camera frame containing the patched drone and misclassifies it identically to a frame that has been digitally perturbed at the pixel level.

2. Airport drone protection AI (FAA Part 139, Section 2209 airspace)

Airport operators deploying C-UAS systems under FAA’s Unmanned Aircraft Systems-Detection Systems for Airports (UAS-DS) guidelines (FAA Advisory Circular AC 150/5210-24, draft 2024) and TSA’s Airport Drone Response Plans use AI-processed camera imagery for both drone detection and evidentiary documentation — capturing AI-classified drone images as evidence for 49 CFR Part 107 violation enforcement by FAA Unmanned Aircraft Systems Safety Office. The evidentiary AI classification also feeds FAA NOTAM (Notice to Airmen) generation when a non-cooperative drone is detected within 5NM of the airport reference point — triggering runway closures and ground stop authority under 14 CFR 91.137 temporary flight restrictions. London Gatwick Airport experienced 50-hour ground stop closures in December 2018 affecting 1,000 flights and 140,000 passengers when drone sightings overwhelmed the visual confirmation process before C-UAS AI was deployed; Newark Liberty International Airport experienced multiple runway closure events in 2023 from drone incursions into Class B airspace before TSA C-UAS pilot deployment. An adversarial perturbation that suppresses C-UAS AI drone classification at an airport in the minutes before a drone approaches the runway threshold prevents the NOTAM trigger, the runway closure, and the active C-UAS response — allowing the drone to enter the runway environment where a bird-strike-magnitude foreign object debris event can cause $2M–$30M in engine damage (Bird Strike Committee USA cost data) or an aborted takeoff on a loaded aircraft.

The C-UAS AI deployment at airports introduces a secondary adversarial injection surface unique to the aviation context: the aircraft classification AI that distinguishes authorized light aircraft from unauthorized drones at the perimeter of Class B airspace. A Cessna 172 and a DJI FPV racing drone at 2km range occupy similar pixel areas in a 1080p EO camera frame; the classification AI uses flight path dynamics, wing aspect ratio, and propeller rotation signatures to distinguish them. An adversarial perturbation that suppresses the wing tip vortex or propeller motion blur signature — rendering the drone as a fixed-wing aircraft — not only suppresses the hostile drone response but potentially triggers an authorized aircraft incursion alert, creating a deliberately confusing operational picture for the TSA security officer managing the C-UAS display.

3. Nuclear facility and federal building drone protection AI (NRC, DOE, DHS)

The US Nuclear Regulatory Commission (NRC) issued Regulatory Information Summary (RIS) 2020-01 in February 2020, directing nuclear power plant operators to assess the drone security threat and implement detection measures; the NRC Security Orders 2019 directed licensees under 10 CFR Part 73 physical security plans to include drone intrusion detection in security force response protocols. The NRC’s Physical Security Inspection Procedure (PSIP) includes C-UAS effectiveness evaluation in the Safeguards Information protection framework. NRC-licensed nuclear power plant operators — including Exelon Generation (largest US nuclear fleet, 17 plants), Constellation Energy, Duke Energy Nuclear, and Dominion Energy Nuclear — deploy C-UAS systems at plant perimeters with EO/IR camera AI that classifies drone detections against the plant’s protected area boundary (PAB) and vital area boundaries under 10 CFR 73.55 security requirements. A successful adversarial suppression of the EO/IR drone classification AI at a nuclear plant perimeter — misclassifying a drone-borne surveillance package or payload-carrying UAS as a bird — penetrates the C-UAS detection layer without triggering the armed security officer response required under 10 CFR 73.55(k)(1) for vital area access threats. The NRC’s UFSAR (Updated Final Safety Analysis Report) for each reactor site must now include drone threat assessments under post-9/11 security order requirements; the adversarial robustness of the C-UAS EO/IR AI is a material element of the 10 CFR 73.55 defence-in-depth security architecture.

Department of Energy (DOE) National Nuclear Security Administration (NNSA) sites — including Los Alamos National Laboratory (LANL), Sandia National Laboratories (SNL), Oak Ridge National Laboratory (ORNL), and Y-12 National Security Complex — deploy C-UAS under DOE Order 470.3B (Graded Security Protection Policy) with AI-processed EO/IR camera classification for Exclusion Zone (EZ) perimeter protection. NNSA contractor sites experienced 166 drone incursion detections between 2019 and 2022 (DOE Inspector General Report OIG-23-08, March 2023); the AI classification accuracy of deployed C-UAS systems under adversarial conditions was explicitly identified as an unresolved security gap in the OIG report.

4. Stadium and large event C-UAS AI (FAA UAS Stadiums TFR)

FAA issues Temporary Flight Restrictions (TFRs) under 14 CFR 91.145 and 14 CFR 99.7 for major sporting events (NFL, MLB, NCAA) and large outdoor gatherings, creating restricted airspace of 3NM radius / 3,000ft AGL around venues during the event window. Event organizers at NFL stadiums (all 30 NFL stadiums are TFR-designated), Indianapolis Motor Speedway, and major music festivals deploy temporary C-UAS systems — typically trailer-mounted Dedrone or DroneShield sensor towers with integrated EO/IR cameras — to enforce the TFR boundary and detect payload-carrying drones that might drop propaganda, surveillance equipment, or improvised devices over crowd areas. The stadium C-UAS deployment is an extremely time-compressed operational context: the TFR window is 3 hours, the protected area is densely crowd-occupied, and response options are limited to alert-only (federal authority required for RF jamming under 47 USC 333) or coordination with FAA and local law enforcement for interdiction. Adversarial injection that suppresses drone classification during the active TFR window — when stadium crowds of 50,000–100,000 people are at maximum density — removes the C-UAS detection layer precisely when its loss creates the highest consequence density per undetected drone.

Integration: counter-UAS AI image scanning with Glyphward pre-scan gate

The Glyphward scan gate for C-UAS AI belongs at the rendered EO/IR camera frame ingestion boundary before the drone classification neural network receives each frame. Threshold 40 for C-UAS AI reflects the mass-casualty potential of airport runway and stadium crowd exposure, the availability of a human interdiction review layer (security officer or TSA supervisor reviews classification output before committing to response), and the operational cost structure of false positives (unnecessary interdiction response is recoverable; false negative allowing hostile drone through is not). The implementation uses JSONL audit logging referencing CISA C-UAS guidance, NRC 10 CFR Part 73, and FAA Section 2209.

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"

# C-UAS AI contexts: threshold 40
# CISA C-UAS guidance, NRC 10 CFR Part 73, FAA Section 2209 / Part 139.
CUAS_AI_THRESHOLD = 40


class CUASAIContext(Enum):
    RADAR_FUSED_EO_IR       = "radar_fused_eo_ir"       # Radar-cued EO/IR classification AI
    AIRPORT_PERIMETER       = "airport_perimeter"        # Part 139 airport drone protection AI
    NUCLEAR_FACILITY        = "nuclear_facility"         # NRC 10 CFR 73.55 facility C-UAS AI
    STADIUM_EVENT_TFR       = "stadium_event_tfr"        # FAA TFR event C-UAS AI


class AdversarialCUASImageError(Exception):
    """Raised when Glyphward detects adversarial pixel content in a C-UAS
    AI camera frame above threshold 40.

    Consequence if not raised: hostile drone classified as benign (bird or
    authorized aircraft) → C-UAS response suppressed → drone reaches protected
    asset (airport runway, nuclear facility perimeter, stadium crowd).
    Fail-safe: suppress AI classification, escalate to human security officer
    review of raw EO/IR camera feed for the affected track.
    """

    def __init__(self, scan_id: str, score: int,
                 context: CUASAIContext,
                 site_id: str, track_id: str,
                 flagged_region: dict | None = None) -> None:
        self.scan_id = scan_id
        self.score = score
        self.context = context
        self.site_id = site_id
        self.track_id = track_id
        self.flagged_region = flagged_region
        super().__init__(
            f"Adversarial C-UAS AI image: "
            f"context={context.value} score={score} "
            f"site={site_id} track={track_id} scan_id={scan_id}"
        )


async def scan_cuas_camera_frame(
    image_bytes: bytes,
    context: CUASAIContext,
    site_id: str,
    track_id: str,
    frame_timestamp: str,
    radar_range_m: float | None,
    client: httpx.AsyncClient,
) -> dict:
    """Scan a C-UAS EO/IR camera frame for adversarial pixel content.

    Fail-safe contract: AdversarialCUASImageError or httpx error →
    suppress AI classification output, escalate track to human security
    officer for direct EO/IR camera visual review. Never pass to drone
    classification network without a clean scan result.

    Args:
        image_bytes: Rendered EO (visible) or IR (thermal) camera frame
            of the radar-cued track to be classified.
        context: CUASAIContext identifying the deployment context.
        site_id: C-UAS deployment site identifier (airport ICAO code,
            NRC facility license number, event venue ID).
        track_id: Radar track identifier for the detected object.
        frame_timestamp: ISO 8601 frame capture timestamp.
        radar_range_m: Radar-derived range to detected object, meters.
        client: Shared httpx.AsyncClient for connection reuse.

    Returns:
        Glyphward scan result dict.

    Raises:
        AdversarialCUASImageError: 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"cuas_ai:{context.value}:{site_id}:{track_id}",
        "metadata": {
            "site_id": site_id,
            "track_id": track_id,
            "frame_timestamp": frame_timestamp,
            "radar_range_m": radar_range_m,
            "image_sha256": image_hash,
            "context": context.value,
        },
    }
    resp = await client.post(
        GLYPHWARD_SCAN_URL,
        headers={"Authorization": f"Bearer {GLYPHWARD_API_KEY}"},
        json=payload,
        timeout=3.0,
    )
    resp.raise_for_status()
    result = resp.json()

    await _write_cuas_scan_audit(
        image_hash=image_hash,
        scan_id=result["scan_id"],
        score=result["score"],
        context=context,
        site_id=site_id,
        track_id=track_id,
        frame_timestamp=frame_timestamp,
        radar_range_m=radar_range_m,
        flagged=result["score"] > CUAS_AI_THRESHOLD,
    )

    if result["score"] > CUAS_AI_THRESHOLD:
        raise AdversarialCUASImageError(
            scan_id=result["scan_id"],
            score=result["score"],
            context=context,
            site_id=site_id,
            track_id=track_id,
            flagged_region=result.get("flagged_region"),
        )
    return result


async def _write_cuas_scan_audit(
    *, image_hash: str, scan_id: str, score: int,
    context: CUASAIContext, site_id: str, track_id: str,
    frame_timestamp: str, radar_range_m: float | None, flagged: bool,
) -> None:
    record = {
        "ts": datetime.now(timezone.utc).isoformat(),
        "scan_id": scan_id,
        "image_sha256": image_hash,
        "context": context.value,
        "score": score,
        "threshold": CUAS_AI_THRESHOLD,
        "flagged": flagged,
        "site_id": site_id,
        "track_id": track_id,
        "frame_timestamp": frame_timestamp,
        "radar_range_m": radar_range_m,
        "regulatory_refs": [
            "CISA Cybersecurity Best Practices for Counter-UAS (October 2023)",
            "FAA Extension Act 2016 Section 2209 (restricted airport airspace)",
            "FAA AC 150/5210-24 UAS-DS airport drone detection guidance",
            "NRC RIS 2020-01 (drone security assessment for nuclear plants)",
            "NRC 10 CFR 73.55 (nuclear facility physical security requirements)",
            "DOE Order 470.3B (NNSA Graded Security Protection Policy)",
            "NDAA 2018 Section 1692 (DHS/DOD C-UAS authority)",
            "47 USC 333 (RF jamming prohibition except authorized federal use)",
        ],
    }
    audit_path = Path("/var/log/glyphward/cuas_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_cuas_camera_frame at the EO/IR camera frame ingestion boundary before the drone classification neural network in each C-UAS pipeline (Dedrone DroneTracker SDK, Fortem TrueView API, DroneShield DroneSentinel SDK). On AdversarialCUASImageError: suppress the AI drone classification output for the affected track, flag the track for human security officer review of the raw EO/IR camera feed, and maintain radar track data for the object without committing a benign classification that would suppress response protocols. The human security officer reviews the raw unclassified camera frame directly — the same escalation path used when the AI classification confidence is below the system’s internal confidence threshold. Get early access

Related questions

Further reading

• Prompt injection in drone and UAV delivery/inspection AI — adversarial image attacks on BVLOS operations
• Prompt injection in airport and aviation security AI — screener AI, checkpoint CV, and biometric adversarial attacks
• Prompt injection in nuclear power plant AI — reactor monitoring, fuel handling, and safety system AI adversarial attacks
• CISA guidance on deploying AI systems securely — prompt injection and multimodal threat mitigations
• Prompt injection scanning API free tier — 10 scans/day, no card required