Tunable Asymmetric Delay Attack (T-ADA)

• T-ADA is a dynamic adversarial technique that introduces configurable, time-varying delays to break the assumed symmetry in communication channels.
• It employs tailored attack profiles—jump, spike, and gradual—to selectively compromise both the short-term precision and long-term stability of synchronization systems.
• Experimental tests on quantum clock synchronization setups show significant increases in time deviation, highlighting vulnerabilities in systems that depend on channel reciprocity.

A Tunable Asymmetric Delay Attack (T-ADA) is a dynamic adversarial technique designed to selectively manipulate synchronization accuracy in time-sensitive systems by precisely controlling and modulating delay asymmetry within communication channels. T-ADA extends traditional asymmetric delay strategies by introducing configurable and time-varying delays, targeting the fundamental reliance of synchronization protocols—particularly quantum clock synchronization (QCS) and high-precision network protocols—on channel reciprocity, thereby undermining their short- and long-term stability across diverse application domains (Han et al., 24 Oct 2025).

1. Formal Definition and Attack Rationale

T-ADA refers to the adversary’s ability to introduce and modulate, at runtime, asymmetric delays independently in each direction of a bidirectional communication link, thereby impacting the computation of clock offsets and synchronization moments. In QCS systems, the correct calculation of the clock difference $\delta$ assumes symmetric propagation delays. T-ADA breaks this symmetry dynamically using hardware elements such as optical circulators or tunable optical delay lines:

$$\delta = \Delta t - \frac{\alpha M(t) + \beta N(t)}{2}$$

where $\Delta t$ is the reciprocal clock difference, $M(t)$ and $N(t)$ are time-varying delays applied to Alice$\to$Bob and Bob$\to$Alice channels, respectively, and $\alpha$, $\beta$ are system-dependent coefficients (typically $\alpha=-1$, $\beta=1$ for round-trip QCS).

T-ADA exploits the inadequacy of static security measures and threshold-based detection, allowing the attacker to stealthily tune the magnitude, onset time, and temporal structure of the induced delay.

2. Attack Mechanism and Trajectory Profiles

T-ADA’s core technical approach involves dynamic delay injection, parameterized by amplitude, temporal profile, and activation epoch:

$$M(t) = A \cdot f(t-t_0) \cdot H(t-t_0), \quad N(t) = A' \cdot f(t-t_0) \cdot H(t-t_0)$$

where $A$ and $A'$ are amplitudes for delay in each direction, $t_0$ is attack initiation time, $f$ is a behavioral function (e.g., constant for jump, pulse for spike, or slowly varying for gradual attacks), and $H$ is the Heaviside step.

Attack profiles identified in (Han et al., 24 Oct 2025) include:

• Jump attacks: Abrupt, persistent change in propagation delay, implementing permanent offset.
• Spike attacks: High-amplitude, short-duration delays causing transient synchronization errors.
• Gradual attacks: Slowly accumulating delay errors, designed to evade static detection thresholds and induce progressive instability.

This parameterization allows the adversary to tailor the attack trajectory to selectively compromise either the short-term precision or long-term stability of the target system.

3. Experimental Demonstration and Key Metrics

Experiments conducted on a 10 km fiber-based QCS testbed used entangled photon pairs (SPDC in PPLN waveguides), optical circulators, motorized delay lines, SNSPD detectors, and atomic clocks to validate T-ADA against real-world protocol implementations (Han et al., 24 Oct 2025). Three representative attack patterns were realized:

Attack Type	Delay Injected (ps)	TDEV Impact (ps@512s)	Stability Outcome
Jump	−10 to −500	Up to 32.05	Severe long-term degradation
Spike	−500 (pulsed)	24.88 (@10s avg)	Short-term disruption
Gradual	−2/−4 per 35 s	68.40 (@1000s avg)	Slow, covert error build-up

Under attack-free conditions, baseline time deviation (TDEV) was observed at ≈1.85 ps, whereas T-ADA increased TDEV up to a factor of 17–37, confirming its efficacy in destabilizing QCS with tailored trajectories.

4. System Vulnerabilities and Detection Evasion

T-ADA leverages two key vulnerabilities:

• Channel reciprocity dependence: Absolute trust that propagation delay is directionally symmetric—assumption shared across QCS, TWFTT, network time protocols (PTP), and TDOA localization protocols (Lee et al., 2019, Delcourt et al., 2019, Li et al., 2022, Finkenzeller et al., 19 Jan 2024, Soltani et al., 12 May 2025).
• Static detection thresholds: Many countermeasures utilize fixed anomaly thresholds, susceptible to evasion by gradual or low-amplitude attacks.

By tuning delays to match expected noise levels, T-ADA can create errors that mimic benign fluctuations, making detection notably difficult. For quantum protocols, non-reciprocal components such as optical circulators used for T-ADA can rotate photon polarization by integer multiples of $180^\circ$ and remain invisible to entanglement-based authentication, yielding measured quantum state fidelities near unity despite significant time discrepancies (Lee et al., 2019).

5. Countermeasures and Defense Strategies

Current literature identifies limitations and potential enhancements in defending against T-ADA:

• Multipath redundant measurements: Cross-validating synchronization over redundant, edge-disjoint paths enables direct estimation of asymmetry (Finkenzeller et al., 19 Jan 2024). Cyclic path asymmetry analysis reports the attack via a measured difference:

$\alpha_{P_0} = RTT_{P_0,P_i} - RTT_{P_i,P_0}$

Immediate offset rectification is performed:

$$\theta_{rect} = \theta_{rep} - \frac{\alpha_{P_0}}{2}$$

• Dynamic clock modeling and time series analysis: Two-state clock models track both offset and frequency error, filtering out fixed offsets and dynamically flagging when observed time deviations exceed computed thresholds (Li et al., 2022).
• Statistical calibration and weighted estimators: TDOA localization systems utilize calibration sources to assign confidence weights to sensor pairs, suppressing the influence of suspect channels in position estimation (Delcourt et al., 2019).
• Deterministic graph-based path analysis: In distributed networks (e.g., UAVs), construction of time-window graphs and path comparison enables deterministic, low-overhead detection of delay-induced routing deviations, even for tunable and asymmetric patterns (Soltani et al., 12 May 2025).

A plausible implication is that future protocols will combine real-time multipath asymmetry estimation, continuous deviation tracking, and systematic exclusion of unreliable channels.

6. Broader Impact and Prospects

T-ADA exposes a fundamental challenge for the integrity of both quantum-enhanced and classical synchronization systems. Its dynamic nature means that critical infrastructure—including secure communications, financial networks, GPS, and networked control—remains susceptible even within entanglement-verified protocols or encrypted channels.

Prospective research directions include:

• Advanced cross-validation architectures: Deployment of statistical cross-validation or redundant path strategies to always monitor direction-specific delays across physically distinct routes.
• Adaptive anomaly detection: Leveraging machine learning and historical time deviation trends to capture both abrupt and gradual delay asymmetries.
• Protocol evolution: Explicit abandonment of symmetry assumptions in protocol design, favoring direct one-way delay measurements and integrated defense logic.

This suggests that securing time synchronization will require protocol-level changes, multi-path redundancy, and continuous delay asymmetry surveillance, especially as quantum networking and time-critical distributed systems proliferate (Han et al., 24 Oct 2025, Finkenzeller et al., 19 Jan 2024).

7. Application Scope and Future Implications (Editor's term: “Adaptive Synchronization Threats”)

By enabling attack profiles to be tailored to the operational demands or the detection regime of a target system, T-ADA provides adversaries with significant leverage to selectively compromise network or quantum system stability. As research continues, a plausible implication is that “adaptive synchronization threats” will be prioritized in assessments of quantum network security, distributed sensor localization, and critical infrastructure time transfer. This trend will likely redefine the baseline for adversarial modeling in time-sensitive applications, motivating a transition from static to paradigm-shifting dynamic security frameworks.