UP STF, Baghpat Police Shoot Dead Rs 4 Crore Robbery Mastermind Sandeep In Encounter

Baghpat: The Uttar Pradesh Special Task Force (STF) and Baghpat police have shot dead a notorious highway robber in an encounter on Sunday. The accused, identified as Sandeep, was wanted in a Rs 4 crore robbery in Kanpur. According to reports, the UP police operation resulted in Sandeep sustaining injuries, following which he succumbed in the hospital during treatment. The police had declared a Rs 1 lakh bounty on his arrest. 

According to reports, the UP STF had been tracking Sandeep for a while, and a team was formed to apprehend him. Meanwhile, on Sunday, on a secret input, the police traced and surrounded him and asked him to immediately surrender. However, Sandeep, in a bid to escape the police, fired upon the cops. The police team retaliated with gunshots, resulting in injuries to the accused. 

After the encounter, Sandeep was injured and taken into custody. He was immediately rushed to the hospital for treatment, where he was declared dead. 

A senior UP Police official stated that the STF had been investigating multiple cases against Sandeep, including the looting of a truck carrying nickel plates worth Rs 4 crore in Kanpur.

Sandeep's Criminal Background

As per the police official, Sandeep had a long history of criminal activities, with over 16 serious cases registered against him across Uttar Pradesh, Maharashtra, and Haryana. These cases included the murder of four truck drivers, and Sandeep had a bounty of Rs 1 lakh on his head. He was known for his involvement in highway robberies, and his modus operandi involved targeting trucks carrying valuable goods.

The UP STF had been conducting searches to apprehend Sandeep, and their efforts finally paid off. The STF's investigation revealed that Sandeep was involved in multiple cases of highway robbery and murder. 

The police stated that the STF's operation was a major success, and it is expected to bring relief to the people of Uttar Pradesh and other states where Sandeep was active.