Survival of the Fittest: New York Times Attackers Evolve Quickly

The attackers behind the breach of the New York Times’ computer
network late last year appear to be mounting fresh assaults that
leverage new and improved versions of malware.

The new campaigns mark the first significant stirrings from the
group since it went silent in January in the wake of a detailed expose
of the group and its exploits — and a retooling of what security
researchers believe is a massive spying operation based in China [1].

The newest campaign uses updated versions of Aumlib and Ixeshe.

Aumlib, which for years has been used in targeted attacks, now
encodes certain HTTP communications. FireEye researchers
spotted the malware when analyzing a recent attempted attack on an
organization involved in shaping economic policy.

And a new version of Ixeshe, which has been in service since 2009 to
attack targets in East Asia, uses new network traffic patterns,
possibly to evade traditional network security systems.

The updates are significant for both of the longstanding malware
families; before this year, Aumlib had not changed since at least May
2011, and Ixeshe had not evolved since at least December 2011.

BACKGROUND

Cybercriminals are constantly evolving and adapting in their
attempts to bypass computer network defenses. But, larger, more
successful threat actors tend to evolve at a slower rate.

As long as these actors regularly achieve their objective (stealing
sensitive data), they are not motivated to update or rethink their
techniques, tactics, or procedures (TTPs). These threat actors’
tactics follow the same principles of evolution – successful
techniques propagate, and unsuccessful ones are abandoned. Attackers
do not change their approach unless an external force or environmental
shift compels them to. As the old saying goes: If it ain’t broke,
don’t fix it.

So when a larger, successful threat actor changes up tactics, the
move always piques our attention. Naturally, our first priority is
ensuring that we detect the new or altered TTPs. But we also attempt
to figure out why the adversary changed — what broke? — so that we can
predict if and when they will change again in the future.

We observed an example of this phenomenon around May. About four
months after The New York Times publicized an attack on its network,
the attackers behind the intrusion deployed updated versions of their
Backdoor.APT.Aumlib and Backdoor.APT.Ixeshe malware families [2].

The previous versions of Aumlib had not changed since at least May
2011, and Ixeshe had not evolved since at least December 2011.

We cannot say for sure whether the attackers were responding to the
scrutiny they received in the wake of the episode. But we do know the
change was sudden. Akin to turning a battleship, retooling TTPs of
large threat actors is formidable. Such a move requires recoding
malware, updating infrastructure, and possibly retraining workers on
new processes.

The following sections detail the changes to Backdoor.APT.Aumlib and Backdoor.APT.Ixeshe.

Backdoor.APT.Aumlib

Aumlib has been used in targeted attacks for years. Older variants
of this malware family generated the following POST request:

POST /bbs/info.asp HTTP/1.1

Data sent via this POST request transmitted in clear text in the
following structure:

<VICTIM BIOS NAME>|<CAMPAIGN ID>|<VICTIM EXTERNAL
IP>|<VICTIM OS>|

A recently observed malware sample (hash value
832f5e01be536da71d5b3f7e41938cfb) appears to be a modified variant of Aumlib.

The sample, which was deployed against an organization involved in
shaping economic policy, was downloaded from the following URL:

status[.]acmetoy[.]com/DD/myScript.js or status[.]acmetoy[.]com/DD/css.css

The sample generated the following traffic:

aumlib1

This output reveals the following changes when compared with earlier variants:

  • The POST URI is changed to /bbs/search.asp (as mentioned,
    earlier Aumlib variants used a POST URI of /bbs/info.asp.)
  • The POST body is now encoded.

Additional requests from the sample generated the following traffic:

aumlib2

These subtle changes may be enough to circumvent existing IDS
signatures designed to detect older variants of the Aumlib family.

The sample 832f5e01be536da71d5b3f7e41938cfb shares code with an
older Aumlib variant with the hash cb3dcde34fd9ff0e19381d99b02f9692.
The sample cb3dcde34fd9ff0e19381d99b02f9692 connected to
documents[.]myPicture[.]info and www[.]documents[.]myPicture[.]info
and as expected generated the a POST request to /bbs/info.asp.

Backdoor.APT.Ixeshe

Ixeshe has been used in targeted attacks since 2009, often against
entities in East Asia [3]. Although the network traffic is encoded
with a custom Base64 alphabet, the URI pattern has been largely consistent:

/[ACD] [EW]S[Numbers].jsp?[Base64]

We analyzed a recent sample that appears to have targeted entities
in Taiwan, a target consistent with previous Ixeshe activity.

ixeshe1

This sample (aa873ed803ca800ce92a39d9a683c644) exhibited network
traffic that does not match the earlier pattern and therefore may
evade existing network traffic signatures designed to detect Ixeshe
related infections.

ixeshe2

The Base64-encoded data still contains information including the
victim’s hostname and IP address but also a “mark” or “campaign
tag/code” that the threat actors use to keep track of their various
attacks. The mark for this particular attack was [ll65].

CONCLUSION

Based on our observations, the most successful threat actors evolve
slowly and deliberately. So when they do change, pay close attention.

Knowing how attackers’ strategy is shifting is crucial to detecting
and defending against today’s advanced threats. But knowing the “why”
is equally important. That additional degree of understanding can help
organizations forecast when and how a threat actor might change their
behavior — because if you successfully foil their attacks, they
probably will.

Notes

[1] http://www.nytimes.com/2013/01/31/technology/chinese-hackers-infiltrate-new-york-times-computers.html?pagewanted=all

[2] This actor is known as APT12 by Mandiant

[3] http://www.trendmicro.com/cloud-content/us/pdfs/security-intelligence/white-papers/wp_ixeshe.pdf

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