Static Indoor Lighting Attack

• Static Indoor Lighting-based Attack (SILA) is a set of techniques that exploits constant indoor illumination to compromise cyber-physical and computational systems through visual, hardware, and communication vulnerabilities.
• It leverages adversarial optimization of fixed light intensity to induce significant degradation in autonomous agent navigation, CMOS side-channel leakage, and protocol-based control of smart lighting devices.
• Mitigation strategies include robust system training, hardware shielding and layout balancing, and enhanced authentication in smart lighting protocols to counteract these adversarial lighting effects.

Static Indoor Lighting-based Attack (SILA) encompasses a family of techniques in which constant artificial lighting conditions are adversarially manipulated to compromise the security, robustness, or integrity of cyber-physical or computational systems. SILA exploits the physical, perceptual, or communication-layer properties of devices and environments under otherwise nominal “static” indoor illumination. Three principal domains have documented distinct threat instantiations: perception attacks on autonomous embodied agents, side-channel leakage in integrated circuits, and protocol-level exploits in smart lighting networks.

1. Conceptual Foundations and Variants

SILA is defined by two essential characteristics: (1) the illumination intensity in the environment is held constant for the targeted period of system operation, and (2) adversarial leverage is obtained either by optimizing the constant intensity value, or by using system-level or network protocol weaknesses to inject persistent lighting states. In contrast to dynamic lighting attacks (such as abrupt on/off switching or rapid modulation), SILA exploits the typical user behavior of “set and forget” lighting in homes and offices.

Major SILA instantiations fall into three research classes:

• Perception attacks against autonomous agents: Adversarial selection of static scene illumination to degrade or mislead visual navigation policies, especially in Vision-and-Language Navigation (VLN) agents (Li et al., 17 Nov 2025).
• Physical side-channel attacks on CMOS hardware: Exploitation of photo-induced static current changes in logic gates under constant modulated light, enabling key extraction via analysis of static power (Petryk et al., 2024).
• Protocol-based attacks on connected lighting infrastructure: Persistent control or hijack of smart bulbs by injecting static lighting states through exploited network protocols (Morgner et al., 2016).

This multiplicity of vectors highlights the multidisciplinary security implications of static lighting manipulation.

2. SILA in Adversarial Perception: VLN Agent Vulnerability

Recent research demonstrates that VLN agents exhibit pronounced vulnerability under adversarially chosen static illumination (Li et al., 17 Nov 2025). The attack scenario models a physical space in which all visible light originates from a single global source of fixed intensity $l^\star \in [l_{\min}, l_{\max}]$. The agent is tasked with following language instructions, often in a simulated household, with lighting remaining constant for the episode length.

The attack process treats $l^\star$ as a black-box variable: at each optimization step, candidates $l^{k+} = l^k + \alpha$ and $l^{k-} = l^k - \alpha$ (clipped within specified bounds) are evaluated via trajectory outcomes. The objective is to maximize a timestep-weighted trajectory loss $\mathcal{J}$:

$$\mathcal{J}(\mathcal{L}) = \sum_{t=1}^{\hat{T}} \frac{t}{\hat{T}} \| p_t - G \|_2,$$

where $p_t$ is the agent's position at step $t$ and $G$ the navigation goal. An $\varepsilon$-greedy heuristic allows exploration to avoid local optima when maximizing $\mathcal{J}$.

Empirical findings:

• For the SPOC VLN agent on ObjectNav, selecting $l^\star \in [0,2]$ causes non-monotonic success rate variances up to $10\%$ as a function of intensity.
• SILA can flip up to $47\%$–$100\%$ of successful navigation episodes into failures, consistently outperforming random intensity selection and texture-based perturbation baselines.
• The effect is robust across both SPOC and FLaRe models and the CHORES benchmark tasks (ObjectNav, Fetch, RoomVisit).

A summary of attack outcomes (SPOC agent):

Task	NoAttack ASR	RandomIntensity ASR	Texture-GA ASR	SILA ASR	NoAttack EL	SILA EL
ObjectNav	0%	23.2%	54.9%	60.4%	115.4	131.1
Fetch	0%	75.0%	87.5%	100.0%	344.7	368.0
RoomVisit	0%	23.8%	50.6%	52.4%	304.1	345.5

Here ASR indicates attack success rate, and EL the median episode length (steps).

This suggests that moderate, realistic lighting variations alone can induce substantial perception failures in VLN systems—highlighting the need for explicit robustness measures against illumination shift.

3. SILA as a Physical Side Channel in CMOS

Experimental work demonstrates SILA as a side-channel attack on digital ICs by exploiting photo-induced static power leakage (Petryk et al., 2024). This attack utilizes the optical-beam-induced current (OBIC) effect, wherein photons with energy above the silicon bandgap are absorbed in reverse-biased PN junctions, generating a data-dependent leakage current observable on the power rails.

The total static current under uniform illumination $\Phi$ and logic state $D$ is:

$$I_{\rm static}(\Phi, D) = I_{\rm dark}(D) + \Delta I(\Phi, D),$$

with the OBIC increment modeled linearly as:

$\Delta I(\Phi, D) = k_{\rm OBIC} \Phi\, \beta(D),$

where $k_{\rm OBIC}$ is a gain factor and $\beta(D)$ encodes the effective illuminated junction area for a gate's input pattern.

Key experimental metrics:

• Individual NAND cell under spot illumination showed per-pattern static current differences of $60\,\mathrm{nA}$ (SNR up to $2$).
• Extrapolation to chip-level “flat-field” illumination predicts differential currents in the milliampere range for designs with $N_{\rm cell} \sim 10^6$.
• Attack range is up to $1\,\mathrm{m}$ with a $1\,\mathrm{W}$ LED and line-of-sight or diffuse illumination. Required modulation depth for reliable key extraction: $\Delta\Phi/\Phi_{\mathrm{amb}} \gtrsim 20\%$.

A step-by-step SILA methodology includes placement of a modulated high-power LED, power-line current acquisition (with lock-in techniques), and correlation-based key extraction (e.g., CPA on recorded amplitude traces).

This demonstrates that even static or slowly-varying indoor light—if adversarially modulated—can form a practicable vector for high-rate side-channel leakage in inadequately shielded chips.

4. Protocol Exploitation in Connected Lighting Systems

SILA also refers to protocol-centric exploits in ZigBee Light Link (ZLL) networks (Morgner et al., 2016). The attack leverages several weaknesses in commissioning and authentication processes for popular smart bulbs (Philips Hue, Osram Lightify, GE Link):

• Inter-PAN Touchlink frames are unauthenticated and unencrypted, allowing an attacker to inject commands for device reset, reveal or change the network key, or permanently “steal” bulbs from an operational network.
• Range limits imposed by received signal strength (RSSI) can be bypassed by increasing transmit power or spoofing RSSI, permitting remote (15–37 m) over-the-air takeovers, substantially exceeding the nominal $2\,\mathrm{m}$ protection.
• The exploitation primitives include active device scanning, hijack via network join commands (using a globally shared ZLL master key for transport), and DoS through unauthorized network or factory reset requests.

Attack performance observed:

• 100% success rates in open environments (Philips Hue, Osram Lightify, GE Link), with attack setup requiring a USRP B200 or Tmote Sky and standard open-source ZigBee exploitation tools.
• Command latency per device: 30–50 ms; multiple bulbs can be targeted in parallel.

The persistence of static-attack states (e.g., forced constant lighting, loss of user control) arises from protocol deficiencies rather than optical or sensor-level vulnerabilities.

5. Countermeasures and Mitigations

Mitigating the various instances of SILA requires context-specific engineering:

• For perception in autonomous agents: Training on diverse lighting conditions, incorporating illumination-awareness in vision stacks, and explicit testing against lighting-induced failures (Li et al., 17 Nov 2025). A plausible implication is that deployment of VLN agents in uncurated real-world environments should not assume strong lighting invariance.
• For CMOS side-channels: Opaque metal shielding of device packages, ambient light sensors for anomaly detection, randomized supply control, layout balancing, and post-processing filters on power rails are recommended (Petryk et al., 2024).
• For connected lighting protocols: Replacing global keys with authenticated per-network key establishment (e.g., ECDH or PAKE), enforcing authenticated and proximity-bounded commissioning, eliminating default key fallbacks, and implementing tamper-resistant key storage (Morgner et al., 2016).

6. Impact and Implications

SILA demonstrates that static indoor lighting, a natively passive environmental factor, functions as a credible adversarial vector across both cyber-physical and networked systems, requiring a cross-layer response spanning algorithmic, physical, and communication security. The concept generalizes beyond active adversarial patterning, exposing latent weaknesses when the system is exposed to realistic yet adversarially chosen static parameters—whether via global illumination, electromagnetic protocol injection, or environmental physical-layer coupling.

The collective literature underscores that SILA is not limited to exotic laboratory setups but reflects structurally exploitable fragilities intrinsic to how modern computational, perception, and network systems interface with their real-world, seemingly static, physical environment.