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  1. 1. Black Ops 2004 @ LayerOne Dan Kaminsky
  2. 2. Introduction Who am I? Senior Security Consultant, Avaya Enterprise Security Practice Author of “Paketto Keiretsu”, a collection of advanced TCP/IP manipulation tools Speaker at Black Hat Briefings  Black Ops of TCP/IP series  Gateway Cryptography w/ OpenSSH Protocol Geek
  3. 3. What’s On The Plate for Today? /* char descrip[256] = “You’ll see”; */
  4. 4. What is DNS DNS: Domain Name System  Mechanism for translating human-readable names into machine routable addresses “Like 411 for the Internet”  As 411 usually but not always yields simple phone numbers, DNS usually but not always yields IP addresses  A: Given name, find IP  MX: Given name, find Mail  PTR: Given IP, find name  TXT: Given name, find “stuff”
  5. 5. “Useful” Traits of DNS (Very Very Abridged) Hierarchical  .com says where to find addresses in, and says where to find addresses in Recursive vs. Iterative Lookups  Iterative Lookup: Ask a server a question, it tells you where to go to find out the answer  Recursive Lookup: Ask a server, it goes out and finds out the answer for you, and tells you  It queries the hierarchy…which you may control Caching  Responses contain a TTL – Time To Live – within which future requests don’t require another message to be sent
  6. 6. Primary Research Areas for DNS Exploitation 1999-2000 were filled with exploits against BIND, the most common DNS server Not terribly vulnerable now DNS Spoofing Returning false addresses = hijack people’s outgoing net connections DNS Tunneling
  7. 7. DNS Tunneling [1] How  Client -> Server  What’s the information for BATCH-OF-ENCODED-  Server -> Client  The information? Why, it’s “HERES-THAT-DATA-YOU- WERE-LOOKING-FOR” Why?  DNS is extremely permeable – it will route through architectures where often nothing else will  Captive portals for Wireless Internet  “More” ;-)
  8. 8. Starting Simple: DNS Tunneling [0] Who? NSTX most popular  Creates a “virtual network device” that routes IP (actually, Ethernet frames) over DNS  Linux Only Rumors of various botnets / malware using DNS as a covert channel
  9. 9. DNS Tunneling[2]: Entering Userspace Starting “Simple”  NSTX requires kernel cooperation to get at IP  Lets make something that doesn’t require the kernel, but still allows remote networking  Remote Networking: “I’m on this network, but all my traffic is routed through that network over there – preferably securely”  Normally done with VPNs (also kernel level)  SSH Dynamic Forwarding allows secure remote networking over a single TCP port (Poor Man’s VPN)  So lets start with SSH over DNS
  10. 10. DNS Tunneling[3]: Problems DNS is not TCP  TCP moves bytestreams, DNS moves records  Blocks of data  TCP lets either side speak first, while in DNS, the server can only talk if the client asks something  TCP is 8 bit clean, while DNS can only move a limited set of characters in each direction (Base64)  This seems so familiar…
  11. 11. DNS Tunneling[4]: Mini-HTTP The semantics of DNS are surprisingly similar to those of HTTP Many tools have been written with the “lets tunnel everything over HTTP” methodology because it gets through firewalls easier (see first point) Those tools that support small message sizes (like GNU httptunnel) can be quickly modified to use DNS as an alternate transport  Must use separate streams for upstream vs. downstream, since downstream reflects all data from upstream (similar to HTTP, but on a per packet basis) But DNS has a feature HTTP servers don’t…
  12. 12. DNS Tunneling[5]: Recursive Redux Recursive lookup: Ask another host to iterate through the hierarchy to find your answer Why, it’s as if every web server was also a web proxy... Simple Trick: Bounce your traffic off any DNS server (like, for instance, a captive portal’s DNS for free WiFi) But there’s better…
  13. 13. DNS Tunneling[6]: Set em up… Some DNS servers are dual hosted  External interface is sending names out  Internal interface is bringing names in  Two interfaces because one is behind the firewall and one is in front Subdomain Delegation  It is possible to claim that “” is hosted at an arbitrary name server with an arbitrary IP address  …even if you yourself can’t route to that address…  …someone else can…
  14. 14. DNS Tunneling[7]: …and knock em down Incoming SSH to Protected Networks via Recursive DNS  1. Wrap SSHD in dns-ized httptunnel  2. Request that remote DNS daemon look up a subdomain of a domain you control. Tell that DNS it’ll find the answer it seeks at the internal server with the SSHD-DNS.  3. SSHD-DNS receives forwarded request, responds to it. Remote DNS daemon forwards that response back to you.  4. Continue with normal SSH over DNS semantics. Note: This is actually a bad thing
  15. 15. Changing the Gameplan Most tricks require a DNS server under the requester’s control  The client and the final server conspire against the recursive server in the middle But what if there is no DNS server under the client’s control?  What can a client do with queries alone?  Can two clients communicate with eachother through a DNS server?
  16. 16. DNS Cache Modulation[0] DNS stores the results of queries, along with a TTL (Time To Live)  Well known Information Leakage: If someone else looked up a site first, the TTL is different.  Example:  root@bsd:~# dig @; sleep 100; dig @  First Reply: 4H IN A  Second Reply: 3h58m19s IN A  Destructive – If nobody else made a particular query, then the probe becomes the standard bearer for max seconds.  Not well known: Two hosts can communicate with eachother through the state change introduced by a particular query  Possibly the lowest bandwidth channel available, at 1 bit per query
  17. 17. DNS Cache Modulation[1] Temporal Bit Mapping  The sender requests a low traffic, preferably low TTL name from the server.  The receiver requests the same name, and sees it at some decremented TTL value. It thus knows at approximately what time the cache entry will expire.  Within some “sender window” after expiration, the sender either does (1) or does not (0) issue another request for the same name  Within some “receiver window”, the receiver checks the name after the window expiration. IF the TTL is max, then the bit was 0. IF the TTL is not max, then the bit was 1.  Capacity = 1 bit per Max TTL period (slowwww)
  18. 18. DNS Cache Modulation[2] “Spatial Bit Mapping”  Spread the load across multiple names, probably each retrieved off a wildcard server  Wildcard – still resolves  Bits are “set” within a setting window  Bits are destructively read within a reading window, using an identical TTL strategy.  Next series of bits may be sent when TTL of last sent request by the sender expires.  Capacity = 1 bit per name per Max TTL period (slow)
  19. 19. DNS Cache Modulation[3] Bidirectionality  Simply use two separate channels, or timeshare one channel  It’s just a shared medium Adaptability  Anything that can be changed by one and seen by another can be used to send data  Web Counters  IP ID counters in IP Stacks  Whether a car was washed  “Did it increment or not? Were there sharp spikes between 6:01 and 6:02?”  “You can always send a bit” – can we send more?
  20. 20. History of DNS Storage Long history of storing data in DNS  Means  TXT records  AXFR Zone Transfer (can be arbitrarily large, but doesn’t work from almost any restricted network since it’s TCP)  Downsides  Very inefficient – packet size (w/o AXFR) is locked below 512 bytes, and you only get a little more than half of the packet filled with a payload  DNS can get really slow, especially under load  Upsides  Everyone has a DNS server, and it caches
  21. 21. KDNS[0] What do we have? A Very Dynamic DNS server A Desire to Send More Than A Bit  Fine, I’ll go host a name on a server A challenge to send something new What do we get?
  22. 22. KDNS[1] Voice over DNS – TXT w/ Streaming Audio Speex codec supports Voice compression at ~2kbps (best public codec)  Ends up (with headers) being about 356 bytes/second  We can traffic 356 bytes per second through even extremely slow DNS servers Power to the Caching People  Use a TTL of WINDOW seconds (~20s)  All listeners behind the same DNS server will split the same “stream”
  23. 23. KDNS[2] Server HOWTO  <timestamp>  TTL=WINDOW  TXT (or MX) = 1.0s or 0.5s of audio   CNAME to <window>.<timestamp>  TTL=0  This may be broken by resolvers with minimum TTL requirements (implemented to fight Dynamic DNS server loads)
  24. 24. KDNS[3] Client HOWTO  Retrieve, learn <window>.<timestamp>  Retrieve audio samples from <timestamp- window> up through <timestamp>  Prefer to start playing at a packet that has spent some time decrementing in the cache – binary search for oldest sample within the window  Add random jitter for the start sample – this way, everyone’s at a different point in the stream, and if the “lead looker” drops, someone else will be next up to be first in line
  25. 25. KDNS[4] Alternate Implementation: Proxy-Friendly HTTP Streaming  Abandon DNS entirely; simply distribute 3-10s chunks of audio over HTTP and let proxy servers share them out  Requires client cooperation, and greatly increases latency, but solves the (bandwidth) problem that proxies don’t cache streams  Proxies don’t support TTLs, though – a server side script would need to monitor that  HTTP of course supports much higher bandwidth!
  26. 26. Crossroads Everything we’ve done with DNS is slow and low bandwidth?  DNS servers store little data, and may return it relatively slowly Can’t we go any faster? Is there no DNS “solution” with capacity?  Many hands make light work  There are many many many DNS servers  And almost all of them cache.
  27. 27. DomainCast[0] The basic concept  Normal DNS operation: Talk to your own server; it retrieves data on demand from the official server upstream  Domaincast DNS operation: Talk to servers with different blocks preloaded into their cache; each of those blocks refers to the location of other cached blocks. Upstream server has actually preloaded data (through directed queries) into all of the servers.  This is possible. This is not necessarily a good idea. Don’t try this at home.
  28. 28. DomainCast[1] Sidestepping Limited Resources  Capacity  20K/server = ~80 records @ 256bytes  700MB = Knoppix, a full Linux distribution  700MB / 20K = 35,000 servers required  Number of DNS servers detected in a single class A: +140,000 (More on this later)  Speed  1Kb/s * 35,000 = 35MB/s  Would require upload of ~3.5MB/s  ~50% packet / formatting overhead = 17MB/s  Not going to achieve peak bandwidth (it’d shred reliability) – but not going to get 1k/s either
  29. 29. DomainCast[2] Packet Formats  Request: <offset>.<filename>  Reply – Either TXT or MX  TXT – Base64 Representation of Fixed Size Struct (describing file size, byte offset, data, and other servers)  MX – Can also represent data as mail server addresses  Requires dots every 64 characters  Responses are reordered randomly – must sort by “precedence”  Some headaches with extra information being “volunteered”  MX may stress BIND servers more (triggers search tree)
  30. 30. DomainCast[3] Basic Mode – All packets retrieved from master server, provide linked list pointers to same host  = first block, here for second  = second block, here for third Etc.
  31. 31. Domaincast[4]: Population via Brute Force Simplest Approach:  Find enough servers that recurse  DNS servers don’t move rapidly -> Scan amortizes well across many populations  Plan out who’s going to store which blocks  Populate servers with data, references to planned other servers  Easy to validate caching, though not easy to fix a broken reference  Can populate out of order – 64 populating, verifying streams
  32. 32. Domaincast[5][0]: Reverse Serial Propagation The problem: To populate the first server, you need the address of the second server. But to populate the second, you need the addr of the third. What to do? Answer: Populate backwards. The last server points nowhere. The second to last points to the last. The third to last points to the second to last.  Multi-server – last server can equal “last server group”.  Server groups may be larger than packet capacity to list servers – just include a random set
  33. 33. Domaincast[5][1]: Reverse Serial Propagation Can be quickly and statelessly deployed  Scan networks with generic recursive probe  For each incoming request seeking to service the probe, return whatever(TTL=0) and probe with an actual block request  If a block request comes back from the recurser, populate the server  If the population packet drops, the upstream should retransmit  Move back through the file after each server group fills up  Can be much slower to populate!
  34. 34. Large Scale DNS Scanning[0] Modding the scanrand codebase…  Basic scanrand concept: One half spews traffic, the other sees what comes back. Little to no communication between the two halves = fast!  Scanrand manages TCP; statelessness is a bit of a novelty to it. DNS is built on UDP, raw packet manipulation isn’t even necessary (it was useful, though).  Reflection: Stateless operation depends on sending packets out and forgetting, only to have the necessary data returned back in the response packet  TCP barely reflects back 48 bits worth of data  DNS reflects back whatever was in the query = thousands of bits if you want ‘em!  Crypto signatures can be embedded in the reflection
  35. 35. Large Scale DNS Scanning[1] Floodable DNS Queries implemented by miname  Is anyone there, recursive or not?  Many hosts that don’t support recursion will at least provide a “NXDOMAIN”, but not all will  We need a generic query that appears to almost always work  PTR -> = localhost  Everyone is localhost   Will you recurse back to me? 
  36. 36. Large Scale DNS Scanning[2] Returned Results Direct: You requested, they responded. Forward Lookup: You requested, they requested back to you, you responded.  Often not the server that you asked that asks you Reverse Lookup: You requested…and someone wondered who you were asking such questions
  37. 37. Large Scale DNS Scanning[3] More on Reverse Auditing  Not being too secretive  root@bsd:~# host  domain name pointer  L0pht’s Antisniff project – locate sniffers and IDS’s by watching reverse DNS traffic on the LAN  Miname w/ Co-opted Servers: Watch sniffers and IDS’s across the entire internet  TTL=0 on PTR replies means they continually look you up when you pass by them  Hard, but not impossible, to associate reverse lookup with node scanned to attract  Temporally associated w/ scan  Multiple scans, out of order, should provide required correlation  Traceroutes may help too – we know who’s looking us up
  38. 38. Rendering The Flood How much data could it be? [root@fire root]# ls -l dns.log -rw-r--r-- 1 root root 360215644 May 28 12:42 dns.log How are we going to visualize this? 3D space – could use the tools we used to visualize the complexity of arbitrary data
  39. 39. Phentropy w/ OpenQVIS
  40. 40. 3D Viz[0] 3D Space provides a potentially valuable perspective on extremely complex datasets Volume tools have gotten more mature Volsuite: Free, Open Source, Full Color, Cross Platform, Fast  Video games = New Absurdly High Speed Graphics Chipsets
  41. 41. 3D Viz[1] “Volumetric” – Texture Based  Stacks of transparent photos  Arbitrary complexity data – it all gets blurred in  Limits positional accuracy  Totally static – you can’t animate the population “Particle” – Vertex Based  Dots! Dots everywhere!  Arbitrary positional accuracy – floating point coordinates can be zoomed into  Limits number of particles  Very easy to make dynamic  Dynamic particles can express higher dimensional data
  42. 42. 3D Viz[2] Dimensions  Static: XYZ, Color  Dynamic  Shape  Direction  Color Shift  Trajectory Shift  Brightness / “Flare”  Surprising aspect of dynamics: Motion away from source point appears to bolster memory / perception of that point
  43. 43. 3D Viz[3] Under Development – As yet unnamed Generic 3D Plotter Coordinates are streamed in, perhaps with information about how to render each particle A more generic version of the “Spinning Cube of Potential Doom”  Parallel development 
  44. 44. Conclusion Stuff = Cool More Stuff Soon