A9 Development: A blog by Agrajag9

http://researchcenter.paloaltonetworks.com/2017/04/unit42-the-blockbuster-sequel/ https://twitter.com/neelmehta/status/864164081116225536 Samples from Unit42: a4b3404fffc581ab06d50f3f2243cb56 cab10f19ae0a6deeb7be7bd0b46a0f5f d511fa33bb3c9a238e4b4eae7bae6e84 WannaCry sample from Neel Mehta: 9c7c7149387a1c79679a87dd1ba755bc All 4 samples share significant overlap in their Rich Headers. In fact, the set of IDs and Value pairs in the Rich Headers from the 3 samples I listed above are fully contained within the set of ID and Value pairs in the Rich Headers of the WannaCry sample from Neel Mehta's Twitter post. The WannaCry sample contains several header pairs that are not present within the other samples - I do not know what tools they represent so I am unable to determine the significance of this particular data point. I don't know how significant this overlap is, but in my own repository that particular set of ID and Value pairs was unique to WannaCry and malware att...

Rich Headers are awesome and you should read about them here: http://www.ntcore.com/files/richsign.htm I was looking at a sample recently and, being the good analyst I am, attempted to automate my DFIR processes using the tools I had handy. Step 1: Get the Rich Headers for the malicious binary. Let's use 9e6658fab423d9b3fabc3578ac5482bf4f21f6fb98949d8ef4f3cad349862b82, a known RAMNIT ransomware sample. $ laika.py 9e6658fab423d9b3fabc3578ac5482bf4f21f6fb98949d8ef4f3cad349862b82 | jq '.scan_result[].moduleMetadata | select(.META_PE) | .META_PE."Rich Header"' { "Checksum": 3183276661, "Hashes": { "SHA1": "dd0f0861bc67028b3cab0d6cdc55a29cc2822f64", "SHA256": "8fd544eee7389c64a29440555f5b968ae0632ed3b989a63402b6d43934a8973e", "MD5": "8451f317057b289c04b2c5b202c09715" }, "Rich Header Values": [ { "Count": 6, "Version": ...