An

An opensource tool for assisted exploit generation on binary programs

Hacker News

An opensource tool for assisted exploit generation on binary programs

Today we are opensourcing an early version of our tool called "Symbolic Exploit Assistant" (SEA) for assisted exploit generation and asking for help/collaboration from the community to expand and improve it. In a few words, this tool starts with a path in a disassembled binary represented with an abstract intermediate language (we start supporting REIL) to generate and solve SMT constraints according to the user request. If the solver finds a solution, the values for the input variables can be used to exploit the path of the program selected. Of course, SEA is not state of the art but a few examples from Gera's Insecure Programming can be "solved". We tried to look for other open source and public tools like SEA, but we couldn't find any. We believe that there should be completely open tools that help people to find vulnerabilities easier and quicker. We don't like the idea that only some companies and governments have access to such tools. This is a very small step in the direction of the democratisation of the access to exploitation tools. Finally, we ask the community for help to do research, development and implementation of SEA in order to build a tool to perform binary analysis. The code and some documentation is available in: https://github.com/neuromancer/sea

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, code, open · Missing: mac, agents, macos
63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released
58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
28%28% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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