So we know a lot of people who've gone into Big Corpo or sold a company or
just worked hard and gotten lucky and happen to be richer than the average
bear. And while a lot of those people put their money into nice things,
nothing wrong with that, a lot of them also try to use that money to change
the world, and then they find out it's harder to change the world with
money than it is with an exploit. And I know a lot of people who say this
out of experience.
I used to say, and I continue to say, that most cyber policy experts have
never seen a real exploit. Yes, even the news reports of 0days are
addictive. They have cool names, like a street drug, they have a shadowy
underworld, they have a bodyguard of rumors and insinuations. Literally, as
I write this, Kaspersky is at CCC doing a street barker presentation on how
much someone thought they were a legitimate target worth throwing down an
iOS 0day chain on, which frankly is not something I would brag about as a
nominally defensive company.
Part of the problem is we analyze exploits out of failures. Reverse
engineering an exploit does not show you the exploit any more than
dissecting a Humboldt squid can show you the terrors of the deep.
Once you've seen an exploit change the world, you are forever stunned. It
is impossible to go backwards to your previous life. It is like your first
taste of sex or love or the feral inhuman joy of combat. For some people,
it's probably better.
But how exploits really work has shockingly little to do with the circus
around exploits on social media or in the news, or press releases from
endpoint security companies or government agencies.
And the same is true with philanthropy. Here we have "Give Miami Day!"
where some random billionaire will match your funds if you give to your
school, which should be getting all the government funding it needs, but
clearly isn't. I don't know if this is at all useful to be honest. It feels
more like covering a hole your government put in your school budget on
purpose.
I tried buying MEL science kits for entire classes of my local grade
school. It worked once, and one of the kids was like "I didn't even know I
liked science". But it was largely impossible to get the kits USED by the
teachers, who are under levels of logistical stress that would stymie a
Marine platoon. So I ended up giving up on this effort.
I supported Project Grapple, which worked well because they have a leader
at the top who is focused on the success of a small set of kids in a very
personal way. But those kinds of leaders are almost impossible to find.
When you find them, it's the best investment you can make if you want to
change the world one kid at a time, which might be the only way. And these
leaders don't last forever.
A lot of big donors focus on things like FIRST Robotics, which frankly has
been a massive success and offers a lifeline for kids in schools where
nothing else matters or meets with any success. It's prohibitively tricky
to figure out which schools have a local leader that can take money and
build a robotics club out of it. It's very much not important that the kids
WIN the competitions - which at the top level are between Northrop Grumman,
Raytheon, and Lockheed Martin engineering teams doing ad-hoc group
apprenticeships. So again, finding these leaders is a constraining problem.
Just like with exploits, what I find is that changing the world with
philanthropy is targeted , personal, and more complicated than it looks. I
have seen very smart people struggle with finding leverage in this space.
Nonprofits are themselves often quite exploitative of their employees or
just generally ineffective.
Anyways, what I'd like to see is the Gulas and Syversons and Alperovitch's
and Pollocks and so forth put a Slack channel together to build a bit of a
body of work on how to do this correctly. A bugtraq for changing the world
with dollars, if you will.
-dave
I think one thing this community does really well, better than almost any
other community I've found, is training. It's amazing, in a way,
because this is a community of professional secret holders. And yet
everywhere you look, a hacker is putting their heart and soul into
iterating on lab exercises for their class in whatever sub-field they are
an expert in.
And giving training is hard. It's hard in the way consulting is hard, but
with even more social activity. On one hand: It's lucrative? But hour for
hour, you're probably better off financially by finding a new bug or doing
consulting work, or any number of other activities than building,
marketing, and running a training class.
When I left my last position, one of the first things I did was pay for and
take Amy Burnette's browser exploitation class. And that's paid off to this
day, really. And there's so many good classes, taught by all the
specialists in our various sub-niches.
It's spectacular that in this world of auto-didacts, we are gifted in the
quantity and quality of training available in our field in a way that is
basically unheard of in any other field.
Of course, there's a lot of things you can't learn from training, and I was
reflecting on this while sitting down and reading the labyrinthine
specifications of some huge protocol for one of my current projects. A lot
of the best bugs I've ever seen hackers find have been from doing exactly
that: They sit and hit page down on some extensively huge and boring
documentation with the steady, persistent rhythm of a neurodivergent
woodpecker pecking at a tree, each tap bringing them closer to the elusive
kernel of truth.
Like, I know people who have various protocol RFCs printed out for long
airplane rides. I've seen hackers read through a book on an operating
system design and then just circle an LPE in the book with a yellow
highlighter.
On the flip side, there are times when you dive headfirst into a colossal
specification, emerging as a veritable guru on an esoteric legacy mail
transfer mess like X.400. Yet, despite this newfound expertise, you find
yourself no more enlightened or advantaged than before, as if you've scaled
a mountain only to find the summit shrouded in the same thick fog that
cloaked its base.
Anyways, happy holidays everyone. Hopefully you had a year of worthy
discoveries.
-dave
Call for Papers 2024
t2 infosec has been pushing the boundaries of security research for two decades and it don't stop. We're back April 18-19, 2024 - Helsinki, Finland.
CFP and registration are both open.
This is an event for newcomers, established merchants of dual use computer code, beginners of vulndev, cyber vagabonds, retired or redacted, and hackers of all sorts.
If you have new original security research targeting old, current or future technology, please submit: https://if.t2.fi/action/call_for_papers
Got 99 problems but an 0day ain't one.
Helsinki offers a Northern European mood, with the resilience built by using Linux on the desktop since 1991, fueled by IRC, demoscene, VHS tapes, invention of modern backpropagation, a lot of memes and sauna. This is the country, which exports Alan Wake 2, quantum computers, and solitude.
As regularly as seasons change, new technology is introduced, vulnerabilities - old and new are discovered, lessons of tradecraft are learned and teachings of the old slowly forgotten.To foster the passion for advancement of (in)security and to keep that fire burning bright for future generations, we gather an intimate crowd of people every year to enjoy the advance of offensive research, share lessons of coming back from the edge, and build those valuable human connections.
Whether your research tackles LLMs, qubits, Azure/AWS/GCP, iOS internals, the market leading or lagging EDR products, access control systems, SoC, BTLE, tipping ChatGPT or something else, which is surprising to either humans or computers, we'd like to hear about it. Security researchers, you know the rules and so do we.
This is an event for the community, by the community. Our focus is on technical excellence, not politics or player hating. Come as you are. The advisory board will be reviewing submissions until 2024-02-04. Slide deck submission final deadline 2024-04-02 for accepted talks.
First come, first served. Submissions will not be returned.
Quick facts for speakers
+ presentation length 60-120 minutes, in English
+ complimentary travel and accommodation for one person[6]
+ decent speaker hospitality benefits
+ no marketing or product propaganda
Still not sure if this is for you? Check out the blast from the past. !!LINK!!
[0] hunter2
[6] except literally @nudehaberdasher and @0xcharlie
Call for Paper submissions
https://if.t2.fi/action/call_for_papers
Buy a ticket to the conference
https://if.t2.fi/action/register/attendees
--
Tomi 'T' Tuominen | Founder @ t2 infosec conference | https://t2.fi
There's not a huge paper I can find on the history of all the big bug
classes. I mean, there's probably not even a real definition of "bug class"
that would survive a drunken conversation between two CTF teams. But like,
screw it, "format strings" are a bug class. "Deserialization bugs" are a
bug class. "strcpy and friends" is a bug class. You know what I mean if
you're the type of person who subscribed to this list.
One thing you know if you internally describe yourself as "hacker" and not
"security professional" is that the bug classes of the future are here now,
but the commercial world doesn't see them yet or sees them as one-off
issues.
For example, check out this amazing slide from this GoSecure presentation (
https://gosecure.github.io/presentations/2019-04-29_atlseccon/History_of_De…
)
[image: image.png]
If you can't see the picture, it was 9 years from "first public instances"
to "bell curve of public mass use of bug class". The nightmare for the
commercial industry of course is that this ignores private research, which
looks like a much bigger bell curve, superimposed on the whole thing as you
all know.
If you've been working on writing automated tools to find bugs, then you
also know the hardest thing to find is a new bug class. We can't all be
Rain Forest Puppy! But even building things to replicate how hackers think
when they just want to find Deserialization or strcpy bugs is hard.
Like, what does it mean to even understand a bug? Bugs are not on one line!
They are not even one concept! Can you inject a bug into a C program
without also injecting another bug you didn't even intend on injecting?
Even though humans are terrible at logic, we somehow think that stepping
logically through a problem is how reasoning works. But you have to get
over this! Stare at an ant farm and chant to yourself the
neo-Buddhist mantra of "This is reasoning. This is cognition" and this way
you quickly realize that precision is not the answer when you try to go
from building a calculating machine to a reasoning machine. Precision and
reasoning are opposites! In other words, the closest we have to reasoning
over program behavior is fuzzing. Once you understand that, a lot of other
stuff starts to make sense.
-dave
I've been working with LLMs for a bit, and also looking at the DARPA Cyber
AI Challenge <https://www.darpa.mil/news-events/2023-08-09>. And to that
end I put together CORVIDCALL which uses various LLMs to essentially 100%
find-and-patch any bug example I can throw at it from the various GitHub
repos that store these things (see below).
[image: image.png]
So I learned a lot of things doing this, and one article that came out
recently (https://www.semianalysis.com/p/google-gemini-eats-the-world-gemini)
talked about the future of LLMs, and if you're doing this challenge you
really are building for future LLMs and not the ones available right now.
One thing they pointed out in that article (which I highly recommend
reading) is that huggingface is basically doing a disservice with their
leaderboard - but the truth is more complicated. It's nice to know which
models do better than other models, but the comparison between them is not
a simple number any more than the comparison between people is a simple
number. There's no useful IQ score for models or for people.
For example, one of the hardest things to measure is how well a model can
handle interleaved and recursive problems. If you have an SQL Query inside
your Python code being sent to a server, does it notice errors in that code
or do they fly under the radar as "just a string".
Can the LLM handle optimization problems, indicating it understands
performance implications of a system?
Can the LLM handle LARGER problems. People are obsessed with context window
sizes but what you find is a huge degradation of accuracy in following
instructions when you hit even 1/8th the context window size for any of the
leading models. This means you have to know how to compress up your tasks
to fit basically into a teacup. And for smaller models, this degradation is
even more severe.
People in the graph database world are obsessed with getting "Knowledge
graphs" out of unstructured data + a graph database. I think "Knowledge
graphs" are pretty useless, but what is not useless is connecting
unstructured data by topic in your graph database, and using that to make
larger community detection-based decisions. And the easiest way to do this
is to pass your data into an LLM and ask it to generate the topics for you,
typically in the form of a Twitter hashtag. Code is unstructured data.
If you want to measure your LLM you can do some fun things. Asking a good
LLM for 5 twitter hashtags in comma separated value format will work MOST
of the time. But the smaller and worse the LLM, the more likely it is to go
off the rails and fail to do it when faced with larger data, or more
complicated data, or data in a different language which it first has to
translate. To be fair, most of them will fail to do the right number of
hashtags. You can try this yourself on various models which otherwise are
at the top of a leaderboard, within "striking distance" on the benchmarks
against Bard, Claude, or GPT-4. (#theyarenowhereclose, #lol)
Obviously the more neurons you have making sure you don't say naughty
things, the worse you are at doing anything useful, and you can see that in
the difference between StableBeluga and LLAMA2-chat, for example, with
these simple manual evaluations.
And this matters a lot when you need your LLM to output structured data
<https://twitter.com/RLanceMartin/status/1696231512029777995?s=20> based on
your input.
So we can divide up the problem of automating finding and patching bugs in
source code in a lot of ways, but one way is to notice the process real
auditors take, and just replicate this by passing in data flow diagrams and
other various summaries into the models. Right now hundreds of academics
are "inventing" new ways to use LLMs. For example "Reason and Act
<https://blog.research.google/2022/11/react-synergizing-reasoning-and-acting…>".
I've never seen so much hilarity as people put obvious computing patterns
into papers and try to invent some terminology to hang their career on.
And of course when it comes to a real codebase, say, libjpeg, or a real web
app, following the data through a system is important. Understanding code
flaws is important. But also building test triggers and doing debugging is
important to test your assumptions. And coalescing this information in, for
example, the big graph database that is your head is how you make it all
pay off.
But what you want with bug finding is not to mechanistically re-invent
source-sink static analysis with LLMs. You want intuition. You want flashes
of insight.
It's a hard and fun problem at the bigger end of the scale. We may have to
give our bug finding systems the machine equivalent of serotonin. :)
[image: image.png]
-dave
https://www.packtpub.com/product/fuzzing-against-the-machine/9781804614976
The authors claim in their conclusion: "We want to stress the importance of
books as journeys to explore and experience topics from the unique
viewpoint of the authors."
And in this they succeeded. This book works best as a proposed curriculum
for a five day workshop for experts to reproduce fuzzing frameworks that
target embedded platforms - including Android and iOS. Largely this is done
by figuring out how to get various emulation frameworks (QEMU in
particular) to carry the weight of virtualizing a platform and getting
snapshots out of it and pushing data into it.
Fuzzing is a childishly easy concept that is composed of devilishly hard
problems in practice (7 and 8 being the ones this book covers in depth -
the fuzzers themselves are simplistic other than those topics):
1. Managing scale
2. Getting decent per-iteration performance
3. Triaging crashes
4. Building useful harnesses
5. Knowing when you have fuzzed enough, vs. being in a local minima
6. Figuring out root causes
7. *Getting your fuzzer to properly instrument your target so you can
have coverage-guided fuzzing*
8. *Handling weird architectures*
9. Generating useful starting points for your fuzzer (or input grammars)
All of these things are basically impossible in the real world. Your
typical experience with a new fuzzing framework is that you install it on a
fresh Linux, pick a target, and then watch as it fails to instrument or
even run.
In other words, just knowing which fuzzer versions to use, and on what, is
valuable information.
When I read a book on security, a good one, I want it to feel like I'm
putting on a brand new powersuit, ready to march into the wilderness with a
flamethrower and a mindset of extreme violence. This book delivers that
feeling. Because while my current business practices have nothing to do
with fuzzing the Shannon baseband, that doesn't mean some small part of me
doesn't want to. We all have the dark urge. We crave SIGSEGV in things
people rely on.
So in summary: 10/10, great book. Would recommend buying 10, setting up a
class, and going over it all together. Of course, this field is RAPIDLY
EVOLVING and you're going to want to get it updated, perhaps with the fancy
new PCODE fuzzer Airbus released earlier today. (
https://github.com/airbus-cyber/ghidralligator)
-dave
The Vegas security conferences used to feel like diving into a river. While
yes, you networked and made deals and talked about exploits, you also felt
for currents and tried to get a prediction of what the future held. A lot
of this was what the talks were about. But you went to booths to see what
was selling, or what people thought was selling, at least.
But it doesn't matter anymore what the talks are about. The talks are about
everything. There's a million of them and they cover every possible topic
under the sun. And the big corpo booths are all the same. People want to
sell you XDR, and what that means for them is a per-seat or per-IP charge.
When there's no differentiation in billing, there's no differentiation in
product.
That doesn't mean there aren't a million smaller start-ups with tiny
cubicles in the booth-space, like pebbles on a beach. Hunting through them
is like searching for shells - for every Thinkst Canary there's a hundred
newly AI-enabled compliance engines.
DefCon and Blackhat in some ways used to be more international as well -
but a lot of the more interesting speakers can't get visas anymore or
aren't allowed to talk publicly by their home countries.
If you've been in this business for a while, you have a dreadful fear of
being in your own bubble. To not swim forward is to suffocate. This is what
drove you to sit in the front row of as many talks as possible at these two
huge conferences, hung over, dehydrated, confused by foreign terminology in
a difficult accent.
But now you can't dive in to make forward progress. Vegas is even more of a
forbidding dystopia, overloaded with crowds so heavy it can no longer feed
them or even provide a contiguous space for the ameba-like host to gather.
Talks echo and muddle in cavernous rooms with the general acoustics of a
high school gymnasium. You are left with snapshots and fragmented memories
instead of a whole picture.
For me, one such moment was a Senate Staffer, full of enthusiasm, crowing
about how smart the other people working on policy and walking the halls of
Congress were - experts and geniuses at healthcare, for example! But if our
cyber security policy matches our success at a health system we are doomed.
I brought my kids this year and it helps to be able to see through the
chaos with new eyes. What's "cool" I asked? in the most boomery way
possible. Because I know Jailbreaking an AI to say bad things is not it,
even though it had all the political spotlights in the world focused on
examining the "issue".
The more crowded the field gets, the less immersion you have. Instead of
diving in you are holding your palm against the surface of the water,
hoping to sense the primordial tube worms at the sea vents feeding on raw
data leagues below you. "Take me to the beginning, again" you say to them,
through whatever connection you can muster.
-dave
https://www.youtube.com/live/YY-ugAHPu4M?feature=share&t=1057
I have on my todo list to reply to our thread last month, but in the
meantime, here is a video that goes over all the lessons learned from my
last couple years doing Neo4j.
But as a reminder, we should still port Ghidra to Neo4j. :)
-dave
There's a new Ghidra release last week! Lots of improvements to the
debugger, which is awesome. But this brings up some thoughts that have been
triggering my vulnerability-and-exploitation-specific OCD for some time now.
Behind every good RE tool is a crappy crappy database. Implicitly we, as a
community, understand there is no good reason that every reverse
engineering project needs to implement a key-value store, or a B-Tree
<https://github.com/NationalSecurityAgency/ghidra/tree/master/Ghidra/Framewo…>,
or partner with a colony of bees which maintain tool state by
various wiggly dances. But yet each and every tool has a developer with
decades of reverse engineering experience on rare embedded platforms either
building custom indexes in a pale imitation of a real DB structure or
engaging in insect-based diplomacy efforts.
I think the Ghidra team (and Binja/IDA teams!) are geniuses, but they are
probably NOT geniuses at building database engines. And reading through the
issues <https://github.com/NationalSecurityAgency/ghidra/issues/985> with
ANY reverse engineering product you find that performance even for the base
feature-set is a difficult ask.
My plea is this: We need to port Ghidra to Neo4j as soon as possible.
Having a real Graph DB store underneath Ghidra solves the scalability
issues. I understand the difficulty here is: There are few engineers who
understand both Neo4j and reverse engineering to the point where this can
be done. I mean, why do it in Neo4j and not PostGres? An argument can be
made for both, in the sense that PostGres is truly Free and the most solid
DB on the market. The pluses for Neo4j are that RE data is typically
graph-based more than linear.
I spent the last two years learning graph dbs, out of some masochistic
desire and ended up getting certified - and I can still RE a little bit. I
will manage the team porting Ghidra to Neo4j if someone funds it. :)
Either way, sooner is better than later. There are so many companies and
people relying on these tools that it seems silly to do anything else.
-dave
P.S. Yes, I remember BinNavi used MsSQL installs for its data, and this was
annoying to install but ... I get why Halvar did it at the time. It's
because he had real work to do and building a DB was not it. I can only
assume Reven doesn't use their own DB? I mean the benefits for
interoperability would be huge between tools. . . like literally everything
you want to do with these tools is better with a real DB underneath.
Lately I've been watching a lot of online security talks - the new thing
for conferences to do is publish them almost immediately, which is amazing.
So like, today I watched Chompie's talk:
https://www.sstic.org/2023/presentation/deep_attack_surfaces_shallow_bugs/
(I was honestly hoping it went from RCE to logic bug and allowed you to log
in, but maybe left as an exercise for the reader).
And yesterday I watched Natalie's talk:
https://www.youtube.com/watch?v=quw8SnmMWg4&t=663s&ab_channel=OffensiveCon
(I'm still a bit confused as to how you connect to a phone's baseband with
SIP, but maybe I will ask later at some point). Does the baseband just have
some TCP ports open for RTC shenanigans? If so, that's great, #blessed, etc.
I actually forgot to post my own talk here, so if you want to watch that,
it's here:
https://www.youtube.com/watch?v=BarJCn4yChA&t=1669s&ab_channel=OffensiveCon
My talk is not actually a call to hack enterprise products - which ya'll
are clearly already doing a lot of (I'm just assuming there are members of
the FIN11 ransomware crew on this list somewhere). It's more about
understanding which business models lead to easy bugs - enterprise software
obviously being one of them, but, for example, DRM components are another
one. There is an endless supply really.
Today I noticed Barracuda is saying that if your appliance gets hacked
<https://www.bleepingcomputer.com/news/security/barracuda-says-hacked-esg-ap…>,
you should replace it immediately. It is now trash, or "e-waste" if you
prefer. This is a surprisingly honest thing to say. Previously appliance
companies who get hacked say things like "Meh? Upgrade pls! Don't forget to
change your passwords!" because as we all know, the firmware
<https://arstechnica.com/information-technology/2023/03/malware-infecting-wi…>
and
boot partitions inside expensive security appliances are all protected by
angry leprechauns, which is why it's still ok in 2023 to have Perl
installed on them, even if you don't know what a ../.../ does in a tar
file.
This honesty would be nice if it also applied to our government agencies -
like instead of this very long report CISA
<https://www.cisa.gov/news-events/cybersecurity-advisories/aa21-110a#:~:text….>
put out about what to do if you think your Pulse Secure VPN was hacked,
which recommends performing a factory reset, updating your appliance to the
very latest version, and then calling your therapist to have a good cry
about it, they should have instead said: "Yes, your appliance did at one
point control authentication for everyone accessing your network, but
because it had issues with gzip files and opening URIs, it is now e-waste."
Crap, I forgot to write about the graph disassembler I want and why.
Tomorrow, for sure.
-dave
