In 2019, a significant US Federal Agency security breach came to light. Hackers infiltrated the facility, stealing 500 megabytes of data from 23 files. Subsequent to this breach, external entities disconnected from the agency’s network. After conducting an extensive audit that spanned months, the agency’s network revealed a connection to an unauthorized Raspberry Pi device.
The compromise of an external user’s account played a pivotal role in the Federal Agency Security Breach. Furthermore, the network was shared, not a segmented environment. Which allowed the attackers to move freely between the various systems within the network causing further damage.
The attackers exploited a Raspberry Pi device that can be easily purchased on Amazon for as little as $25, adding a concerning layer to the Federal Agency Security Breach. Originally conceived as an affordable tool to provide students with low-cost computers and free software, this unassuming barebones computer has taken a sinister turn in its application. Hackers have harnessed the device’s compact, credit card-like dimensions, capitalizing on its array of hacking tools. Particularly its ability to surreptitiously collect data from targeted networks.
The Raspberry Pi supports a variety of payloads and scripts. Once mounted on, the device can perform Network Packet sniffing. Used mainly for reconnaissance purposes. Some more advance payloads include an easy to use 802.1x bypassing module which helps the attacker overcome various MAC authentication procedures used by some of the NAC vendors. Exfiltration of data from the Raspberry Pi can easily be done by connecting a mass storage device to it, use its on board WiFi capabilities or, for more covert operations, a dedicated USB-Wireless Dongle (non-WiFi) can be used, making its detection more difficult.
The Federal Agency Security Breach Audit
The audit uncovered that the Federal Agency Security Breach had resulted in the agency’s reduced visibility into devices connected to its network. Thereby hindering the ability to comprehensively secure those networks. The considerable depth in which the attackers went provided them with access to several sensitive operations. When news broke, several connected agencies disengaged from the network to prevent further damage.
Hardware Asset Visibility and Policy Enforcement to Mitigate Federal Agency Security Breaches
Many times, enterprises’ IT and security teams struggle in providing complete and accurate visibility into their hardware assets. Especially in today’s extremely challenging IT/OT/IoT environment. This challenge has been further exacerbated by the Federal Agency Security Breach. This is due to the fact that often, there is a lack of visibility. Which leads to a weakened policy enforcement of hardware access. This may result in security accidents, such as ransomware attacks, data leakage, etc.
To address the challenge of Federal Agency Security Breach, it’s essential to maintain a comprehensive awareness of your hardware assets. Regardless of their characteristics and the interface utilized for connections by potential attackers. Moreover, it is important to be practical and adjust to the dynamic Cyber security defenses put in place to block them, as well as take advantage of the “blind” spots. Mainly through USB Human Interface Device (HID) emulating devices or Physical layer network implants.
In addition to the deep visibility layer, a comprehensive policy enforcement mechanism recommends on best practice policy and allows the administrator to define a strict, or more granular policies, set of rules for the system to enforce.
Sepio is the leader in the Rogue Device Mitigation (RDM) market. And is disrupting the cybersecurity industry by uncovering hidden hardware attacks operating over network and USB interfaces, addressing the challenge of Federal Agency Security Breach.
Sepio is the only company in the world to undertake physical layer visibility fingerprinting. HAC-1 Hardware Access Control, calculates a digital fingerprint using the device descriptors of all connected peripherals and compares them against a known set of malicious devices, automatically blocking any attacks. With Machine Learning, the software analyses device behavior to identify abnormalities, such as a mouse acting as a keyboard.Download Case Study