From 1b353700abbedef92ab4015be867a633b7b924c7 Mon Sep 17 00:00:00 2001 From: Philipp Nowak Date: Sun, 19 Jan 2020 17:32:38 +0100 Subject: [PATCH] Add write-up for OTW day-25: Lost in Maze --- writeups/litplus/otw19.md | 115 ++++++++++++++++++++++- writeups/litplus/otw19/25-close.png | Bin 0 -> 25557 bytes writeups/litplus/otw19/25-driver.png | Bin 0 -> 15814 bytes writeups/litplus/otw19/25-home.png | Bin 0 -> 23987 bytes writeups/litplus/otw19/25-initscreen.png | Bin 0 -> 207768 bytes writeups/litplus/otw19/25-mapping.png | Bin 0 -> 216975 bytes writeups/litplus/otw19/25-something.png | Bin 0 -> 955 bytes writeups/litplus/otw19/A0Intro.md | 2 +- writeups/litplus/otw19/Adv-24.md | 6 +- writeups/litplus/otw19/Adv-25.md | 107 ++++++++++++++++++++- 10 files changed, 220 insertions(+), 10 deletions(-) create mode 100644 writeups/litplus/otw19/25-close.png create mode 100644 writeups/litplus/otw19/25-driver.png create mode 100644 writeups/litplus/otw19/25-home.png create mode 100644 writeups/litplus/otw19/25-initscreen.png create mode 100644 writeups/litplus/otw19/25-mapping.png create mode 100644 writeups/litplus/otw19/25-something.png diff --git a/writeups/litplus/otw19.md b/writeups/litplus/otw19.md index b444b63..1d50ff3 100644 --- a/writeups/litplus/otw19.md +++ b/writeups/litplus/otw19.md @@ -11,7 +11,7 @@ Challenges participated in: * day-24: Got shell? `solved` `web, linux` - 2.25 hours * day-25: Lost in Maze `solved` `misc` - 16.00 hours -Total time spent: 39.75 hours (+ 6.5 hours for writeups) -- More effort TBD +Total time spent: 39.75 hours + 8 hours for writeups ~> 48 hours total. ### Personal Reflection Since the guidelines require that the whole writeup be in a single file, this has been merged by a very sophisticated shell script. The source files for the individual challenges are located in the [`writeups/litplus/otw19`](/writeups/litplus/otw19) directory and match the RegEx `Adv-\d{2}.md`. @@ -456,14 +456,16 @@ day-24 Got shell? ----------------- OverTheWire Advent 2019, day 24 -Time spent for this challenge: 2.25 hours +Discussion in [Mattermost](https://mattermost.w0y.at/otw-advent-2019/channels/day-24-got-shell) + +Time spent for this challenge: 2.25 hours + 0.5 hours write-up * December 24: 2.25 hours (13:30-15:45) ### Overview ``` -Got shell? web, linux +Got shell? - web, linux Points: 1337 Can you get a shell? NOTE: The firewall does not allow outgoing traffic & There are no additional paths on the website. @@ -687,12 +689,115 @@ day-25 Lost in Maze ------------------- OverTheWire Advent 2019, day 25 -Time spent for this challenge: 16 hours +Discussion in [Mattermost](https://mattermost.w0y.at/otw-advent-2019/channels/day-25-lost-in-maze) + +Full exploit on [GitLab](https://gitlab.w0y.at/ctfs/overthewire-advent-bonanza-2019/tree/master/day-25) + +Time spent for this challenge: 16 hours + 1 hour write-up * December 25: 7 hours (15:30-19:30, 21:00-00:00) * December 26: 9 hours (00:00-09:00) -Writeup TBD +### Overview + +``` +Lost in Maze - misc +Points: 1337 + +We have a code red ( and green, gold and white), we have lost Santa. +While he was delivering gifts he seems to have gotten a bit tipsy and +got lost somewhere over Europe. Luckily we saw this coming, we have a +GPS and bodycam on him, but we need your help. Get him out of there and +back on track to save Christmas. + +Service: nc 3.93.128.89 1225 +Author: b0bb +``` + +Upon connection, the game presents a fancy splash screen and asks for a screen size: + +![Start screen](otw19/25-home.png) + +After entering some screen size, the game renders a maze in ASCII art. In the bottom right corner, a mini-map is displayed. + +![Game screen](otw19/25-initscreen.png) + +Navigation is possible via WASD. + +### Exploitation +#### Mapping the search space +I quickly realised that the whole game state can be represented using the little GPS map. Initially, I thought that by turning in specific increments, moving would be possible in straight lines. However, this turned out to be incorrect. The rotations do not allow to move in 90° angles, and the client needs to adjust to the 3D rotation. The exact rotation is not visible in the map, but can be inferred from the big screen. So the first approach I tried was to manually walk through the labyrinth and record the map to have some orientation. + +After some Python™, I arrived at this state: + +![Small map reconstructed to big map](otw19/25-mapping.png) + +The setup for this consists of [`client.py`](https://gitlab.w0y.at/ctfs/overthewire-advent-bonanza-2019/blob/35e34e8facfce4863ecabc0cb84374da3742e6c6/day-25/client.py) and a [`model.py`](https://gitlab.w0y.at/ctfs/overthewire-advent-bonanza-2019/blob/35e34e8facfce4863ecabc0cb84374da3742e6c6/day-25/model.py). The former handles the connection itself and the interaction logic, while the latter is responsible for modelling the game state as objects and operations on them. + +What the client does at this point is connect to the server, input the screen size, and receive the screen. This is then output to the console. A command prompt allows to move via WASD and export the map to a file. After the sctreen is read, it is also passed to the model. + +This first parses the `ScreenState` to find the GPS and provides the GPS parser with the contents of the GPS itself. It also strips the colour codes used to make the game visually attractive. These are not helpful for parsing content. `GpsState` then takes the GPS lines and parses the current position and target position. Then, it looks for the cursor as center point of reference. next, it parses all walls from the GPS and stores their positions relative to the center. Finally, it adds the current positon to the relative positions to obatain absolute positions of the walls observed. It stores a boolean array of walls for absolute coordinates. Finally, the `Map` object puts this all together and keeps track of current position and map, merging in new information from the GPS for every new screen. It also takes care of printing the map to a string. + +Further, I deduced that distance is encoded as follows: + +``` +` very far +- +~ +: +^ +r += +x +z +e +m +& +& +M +N +Q +@ no distance +``` + +If more than half of the screen is made up of `@` characters, movement is denied. This is a useful heuristic to use later for automatic movement. + +#### Directions for the driver +Using fully manual control, I was only able to get to around `(30, 25)` in the 10 minutes of timeout. This is not very good since the target is `(81, 81)`. So, I decided that an algorithm was needed to suggest where I should go. + +Like a true hacker, I got inspired by someone else's A-Star implementation for this: [Thanks Laurent Luce](https://www.laurentluce.com/posts/solving-mazes-using-python-simple-recursivity-and-a-search/). The implementation wasn't quite perfect and didn't fit all of our needs, so I had to adapt it. The main issue with this implementation of A-Star is that it is designed for a *known* search space, which we do not have. We only know about a small part of the maze via the mini-map. Importantly, heuristics are not stable and previously-promising tiles may lead to a dead end in the next update. + +What the A-Star does is to discover new tiles adjacent to the current position and assign each one a heuristic value. The next tile to visit is the tile with the lowest heuristic value (that is supposed to represent the effort needed to get there). I used square distance to the goal as heuristic. + +The way this is implemented in the [final `model.py`](https://gitlab.w0y.at/ctfs/overthewire-advent-bonanza-2019/blob/master/day-25/model.py#L179) is that after receiving an update, we creates a new `TilePathFinder` to suggest the next target. This stores the current candidate list in a heap (`open`). In `process`, it loops as long as there are candidate tiles. If the next tile is the goal, we return it (the `PqTile` model keeps track of the path we used to get there). Otherwise, we look at its neighbours, compute their heuristic, and add them to the candidate heap. + +Later, I changed this portion of the algorithm to penalise already-visited tiles, because the bot kept getting stuck in locally-optimal solutions that did not take into account that not all of the maze was discovered. I also added a special tile state, `MYSTERY`, to represent that a tile's value is unknown. Later, I modified the algorithm to look primarily at such mystery tiles, because discovering more of the map is far more helpful than staying in the map. (The initial solution went to the tile that was closest to the goal of all known tiles, but this did not always reveal a closer tile.) + +To assist the driver, I visualised visited tiles (blue), the current position (green) and the current A-Star target in the full map: + +![Map with colours](otw19/25-driver.png) + +#### Automating it +However, manual control was still too slow to reach the goal in a reasonable time-frame. I started adding [a small heuristic algorithm](https://gitlab.w0y.at/ctfs/overthewire-advent-bonanza-2019/blob/master/day-25/client.py#L185) to move straight ahead until an obstacle was encountered. This worked, but still needed manual control, had no parallelisation, and couldn't turn or go to a target. + +Next, I added [a more complicated algorithm](https://gitlab.w0y.at/ctfs/overthewire-advent-bonanza-2019/blob/master/day-25/client.py#L103) that keeps track of the A-Star target and past moves. It uses the path information from `PqTile` to move to the A-Star target. If it detects that it gets stuck (this works by analysing the wall distances and if the last few moves changed the screen view), it does some random movement to get unstuck. It also autonomously turns to face the correct direction. Since this works fully automatically, I was able to run this in parallel with three instances running locally and six on a server I own, using Docker. + +Applying this and incrementally improving the algorithm with different heuristics yielded quite successfuly executions like this: + +![close to the goal](otw19/25-close.png) + +![](otw19/25-something.png) + +After reaching the goal for the first time, I learned that `except Exception` does not catch errors in Python, and lost the flag. Sad + +One and a half hours later, the algorithm succeeded again, this time with proper error handling :) + +``` +You saved Christmas, here is your reward: +AOTW{426573742077697368657320666F7220323032302121} +``` --- +Thanks for reading all of this, lol + diff --git a/writeups/litplus/otw19/25-close.png b/writeups/litplus/otw19/25-close.png new file mode 100644 index 0000000000000000000000000000000000000000..dfc673d5a5015aa226ac3000b79af0733aeaf74d GIT binary patch literal 25557 zcmbrm1z1#F7dAXL1|qf~poEGD7<8&2Fm!hrNJ*FEUJ(t%5oSAdZjN5(PF~#DAZOH6l&XJ zlAUm6f^+9p__5vUI$Dtg{&OO^^Bn#@VSPi@TEXnDwcSliJ(RwwnTZ~=)on{XJyR*ZZDHK}xn&N%`L87CsVt3B)*xB3I>jzoBNY zD<=cmuGJau&9d|WX<@3%QmKztVZ^jq@!Y;3m-!fKr_s~sYArkW%Dt?7No9U`gQ`*|@z z^Fu;sv1go0lJ@+VCnnTKXE`JxZAS`DAW5S7Sl;Xn{)NHoH-v>#XVrv+9%-`p?5F07 zHglP{1jE9}##TF>tKnFlgZ0N+x8-lqz)MI;nbf!ttT^uX1Pn3b8Zp9D7w`V?AxxON zH(ie3GU{3QjBH@Ey^um+(ROKR1f!y)?< zL~DhYM_FB@i#=&LwPB2P)YXl$S0d-92BOTI2jsNuhutcTJX 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n$GERA$GyNatK;FH@*Fc?$c)@hc&vH&>sduPHQD?pMgji=Fv{^- literal 0 HcmV?d00001 diff --git a/writeups/litplus/otw19/25-something.png b/writeups/litplus/otw19/25-something.png new file mode 100644 index 0000000000000000000000000000000000000000..a999b050a4dfc79bb3f92bfbc15bf25000435ce1 GIT binary patch literal 955 zcmeAS@N?(olHy`uVBq!ia0vp^fk1qagAGV-e;HE>q*#ibJVQ8upoSx*1IU*wag8Vm z&QB{TPb^AhNYBg9P1P+cKnp_)9RYMIpn#SlkW-ckU5)M6!tEK(`PX-qKD>Q`rXCnWi~n|LefYBW`+9%Y%_&kvNI+CH z%sp(jH#oLb-Kw@`75Cxo+JXr#4T6krED|ur9m#F0-^DC1YX4kPQTP3m`^#THR~KHp zzP=e#smRX5h3^XDN}uoI{+_WsrW4s6AnO*~Fg^2(@AAq!x7~J~z~a>Xj+;OtuNGmBAvp;9K#wa^A;ZGL>`hTz>0?a6Z%@KQ`?37Qc7FqZl&; z7QMS|G*4!aMr?h|pEX~9-gzd3jQ3 zXc5rEao~S*iq?1DKK}dr^z0AJefu{BEr_q|+dTKxql>jWu>_e%dEfGvenqZd0_$qA zxU6~g>ig!o`#ZFDS*$Uyt5eFgkU947;opvq9sB+X-+sI8Fbj_p0%~> zYn80H_}Txw%fAaMD@{9{dL}JCy87SVj*6lpnZ*}1^z`>1ud&Pj^YgpXqn|&|Or7!i zzt^1{v%;!hJ3l@zKf-8{!_4;h 48 hours total. ### Personal Reflection Since the guidelines require that the whole writeup be in a single file, this has been merged by a very sophisticated shell script. The source files for the individual challenges are located in the [`writeups/litplus/otw19`](/writeups/litplus/otw19) directory and match the RegEx `Adv-\d{2}.md`. diff --git a/writeups/litplus/otw19/Adv-24.md b/writeups/litplus/otw19/Adv-24.md index cf28a88..8c42073 100644 --- a/writeups/litplus/otw19/Adv-24.md +++ b/writeups/litplus/otw19/Adv-24.md @@ -2,14 +2,16 @@ day-24 Got shell? ----------------- OverTheWire Advent 2019, day 24 -Time spent for this challenge: 2.25 hours +Discussion in [Mattermost](https://mattermost.w0y.at/otw-advent-2019/channels/day-24-got-shell) + +Time spent for this challenge: 2.25 hours + 0.5 hours write-up * December 24: 2.25 hours (13:30-15:45) ### Overview ``` -Got shell? web, linux +Got shell? - web, linux Points: 1337 Can you get a shell? NOTE: The firewall does not allow outgoing traffic & There are no additional paths on the website. diff --git a/writeups/litplus/otw19/Adv-25.md b/writeups/litplus/otw19/Adv-25.md index 9d30181..719b484 100644 --- a/writeups/litplus/otw19/Adv-25.md +++ b/writeups/litplus/otw19/Adv-25.md @@ -2,12 +2,115 @@ day-25 Lost in Maze ------------------- OverTheWire Advent 2019, day 25 -Time spent for this challenge: 16 hours +Discussion in [Mattermost](https://mattermost.w0y.at/otw-advent-2019/channels/day-25-lost-in-maze) + +Full exploit on [GitLab](https://gitlab.w0y.at/ctfs/overthewire-advent-bonanza-2019/tree/master/day-25) + +Time spent for this challenge: 16 hours + 1 hour write-up * December 25: 7 hours (15:30-19:30, 21:00-00:00) * December 26: 9 hours (00:00-09:00) -Writeup TBD +### Overview + +``` +Lost in Maze - misc +Points: 1337 + +We have a code red ( and green, gold and white), we have lost Santa. +While he was delivering gifts he seems to have gotten a bit tipsy and +got lost somewhere over Europe. Luckily we saw this coming, we have a +GPS and bodycam on him, but we need your help. Get him out of there and +back on track to save Christmas. + +Service: nc 3.93.128.89 1225 +Author: b0bb +``` + +Upon connection, the game presents a fancy splash screen and asks for a screen size: + +![Start screen](25-home.png) + +After entering some screen size, the game renders a maze in ASCII art. In the bottom right corner, a mini-map is displayed. + +![Game screen](25-initscreen.png) + +Navigation is possible via WASD. + +### Exploitation +#### Mapping the search space +I quickly realised that the whole game state can be represented using the little GPS map. Initially, I thought that by turning in specific increments, moving would be possible in straight lines. However, this turned out to be incorrect. The rotations do not allow to move in 90° angles, and the client needs to adjust to the 3D rotation. The exact rotation is not visible in the map, but can be inferred from the big screen. So the first approach I tried was to manually walk through the labyrinth and record the map to have some orientation. + +After some Python™, I arrived at this state: + +![Small map reconstructed to big map](25-mapping.png) + +The setup for this consists of [`client.py`](https://gitlab.w0y.at/ctfs/overthewire-advent-bonanza-2019/blob/35e34e8facfce4863ecabc0cb84374da3742e6c6/day-25/client.py) and a [`model.py`](https://gitlab.w0y.at/ctfs/overthewire-advent-bonanza-2019/blob/35e34e8facfce4863ecabc0cb84374da3742e6c6/day-25/model.py). The former handles the connection itself and the interaction logic, while the latter is responsible for modelling the game state as objects and operations on them. + +What the client does at this point is connect to the server, input the screen size, and receive the screen. This is then output to the console. A command prompt allows to move via WASD and export the map to a file. After the sctreen is read, it is also passed to the model. + +This first parses the `ScreenState` to find the GPS and provides the GPS parser with the contents of the GPS itself. It also strips the colour codes used to make the game visually attractive. These are not helpful for parsing content. `GpsState` then takes the GPS lines and parses the current position and target position. Then, it looks for the cursor as center point of reference. next, it parses all walls from the GPS and stores their positions relative to the center. Finally, it adds the current positon to the relative positions to obatain absolute positions of the walls observed. It stores a boolean array of walls for absolute coordinates. Finally, the `Map` object puts this all together and keeps track of current position and map, merging in new information from the GPS for every new screen. It also takes care of printing the map to a string. + +Further, I deduced that distance is encoded as follows: + +``` +` very far +- +~ +: +^ +r += +x +z +e +m +& +& +M +N +Q +@ no distance +``` + +If more than half of the screen is made up of `@` characters, movement is denied. This is a useful heuristic to use later for automatic movement. + +#### Directions for the driver +Using fully manual control, I was only able to get to around `(30, 25)` in the 10 minutes of timeout. This is not very good since the target is `(81, 81)`. So, I decided that an algorithm was needed to suggest where I should go. + +Like a true hacker, I got inspired by someone else's A-Star implementation for this: [Thanks Laurent Luce](https://www.laurentluce.com/posts/solving-mazes-using-python-simple-recursivity-and-a-search/). The implementation wasn't quite perfect and didn't fit all of our needs, so I had to adapt it. The main issue with this implementation of A-Star is that it is designed for a *known* search space, which we do not have. We only know about a small part of the maze via the mini-map. Importantly, heuristics are not stable and previously-promising tiles may lead to a dead end in the next update. + +What the A-Star does is to discover new tiles adjacent to the current position and assign each one a heuristic value. The next tile to visit is the tile with the lowest heuristic value (that is supposed to represent the effort needed to get there). I used square distance to the goal as heuristic. + +The way this is implemented in the [final `model.py`](https://gitlab.w0y.at/ctfs/overthewire-advent-bonanza-2019/blob/master/day-25/model.py#L179) is that after receiving an update, we creates a new `TilePathFinder` to suggest the next target. This stores the current candidate list in a heap (`open`). In `process`, it loops as long as there are candidate tiles. If the next tile is the goal, we return it (the `PqTile` model keeps track of the path we used to get there). Otherwise, we look at its neighbours, compute their heuristic, and add them to the candidate heap. + +Later, I changed this portion of the algorithm to penalise already-visited tiles, because the bot kept getting stuck in locally-optimal solutions that did not take into account that not all of the maze was discovered. I also added a special tile state, `MYSTERY`, to represent that a tile's value is unknown. Later, I modified the algorithm to look primarily at such mystery tiles, because discovering more of the map is far more helpful than staying in the map. (The initial solution went to the tile that was closest to the goal of all known tiles, but this did not always reveal a closer tile.) + +To assist the driver, I visualised visited tiles (blue), the current position (green) and the current A-Star target in the full map: + +![Map with colours](25-driver.png) + +#### Automating it +However, manual control was still too slow to reach the goal in a reasonable time-frame. I started adding [a small heuristic algorithm](https://gitlab.w0y.at/ctfs/overthewire-advent-bonanza-2019/blob/master/day-25/client.py#L185) to move straight ahead until an obstacle was encountered. This worked, but still needed manual control, had no parallelisation, and couldn't turn or go to a target. + +Next, I added [a more complicated algorithm](https://gitlab.w0y.at/ctfs/overthewire-advent-bonanza-2019/blob/master/day-25/client.py#L103) that keeps track of the A-Star target and past moves. It uses the path information from `PqTile` to move to the A-Star target. If it detects that it gets stuck (this works by analysing the wall distances and if the last few moves changed the screen view), it does some random movement to get unstuck. It also autonomously turns to face the correct direction. Since this works fully automatically, I was able to run this in parallel with three instances running locally and six on a server I own, using Docker. + +Applying this and incrementally improving the algorithm with different heuristics yielded quite successfuly executions like this: + +![close to the goal](25-close.png) + +![](25-something.png) + +After reaching the goal for the first time, I learned that `except Exception` does not catch errors in Python, and lost the flag. Sad + +One and a half hours later, the algorithm succeeded again, this time with proper error handling :) + +``` +You saved Christmas, here is your reward: +AOTW{426573742077697368657320666F7220323032302121} +``` --- +Thanks for reading all of this, lol + -- 2.43.0