Q2: Why did you call attack the Glowworm attack?īoth the attack and the insect develop from a bug that emits light. In addition, many devices lack dedicated means of countering this phenomenon. In many devices, the power indicator LED is connected directly to the power line.Īs a result, the intensity of a device's power indicator LED is correlative to the power consumption. Q1: Why do devices leak information from their power indicator LED? We propose an optical-audio transformation (OAT) to recover sound by isolating the speech from the optical measurements obtained by directing an electro-optical sensor at a device's power indicator LED.įinally, we test the performance of the Glowworm attack in various experimental setups and show that an eavesdropper can apply the attack to recover speech from a speaker's power indicator LED with good intelligibility from a distance of 15 meters and with fair intelligibility from 35 meters.

We analyze the response of the power indicator LED of various devices to sound and show that there is an optical correlation between the sound that is played by connected speakers and the intensity of their power indicator LED due to the facts that: (1) the power indicator LED of various devices is connected directly to the power line, (2) the intensity of a device's power indicator LED is correlative to the power consumption, and (3) many devices lack a dedicated means of countering this phenomenon.īased on our findings, we present the Glowworm attack, an optical TEMPEST attack that can be used by eavesdroppers to recover sound by analyzing optical measurements obtained via an electro-optical sensor directed at the power indicator LED of various devices (e.g., speakers, USB hub splitters, and microcontrollers). In this paper, we identify a new class of optical TEMPEST attacks: recovering sound by analyzing optical emanations from a device’s power indicator LED. Two main classes of optical TEMPEST attacks against the confidentiality of information processed/delivered by devices have been demonstrated in the past two decades the first class includes methods for recovering content from monitors, and the second class includes methods for recovering keystrokes from physical and virtual keyboards.

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