![0xKaden's tweet photo. yETH Exploit Deep Dive
After spending some time exploring the recent yETH exploit, I quickly realized that it's easily one of the most sophisticated attacks I've ever seen. In fact, it was so complicated that every writeup I read misunderstood at least some part of the attack. This complexity provides for some serious alpha to developers and security researchers who can thoroughly understand the attack, so don't just bookmark this, let's dive in.
Hybrid AMM Curve
To understand this exploit, we first need to understand the underlying mechanism of the protocol. The yETH pool uses an invariant which is a hybrid between constant product and constant sum. If you're familiar with the inner workings of Uniswap, you should be familiar with the constant product behavior, essentially it just adjusts the price according to the reserves. Whereas constant sum results in a constant price between the tokens, regardless of reserves. The yETH hybrid curve behaves like a constant sum when the token reserves are balanced, keeping the price constant, and behaves like a constant product curve when the reserves are imbalanced. This behavior is valuable for pools of assets which have the same value due to the fact that the price is much less sensitive to reserve changes.
Below we have a graph [1] of these different curves. Red: constant product, green: constant sum, blue: hybrid used by the yETH pool.
The First Bug: Breaking The Invariant
Let's zoom in on the `_calc_supply` function. This function uses an iterative approximation to converge to a new supply and constant product term at each iteration, ending the loop once sufficient precision is achieved.
The constant product term (r) is recomputed at each iteration as the current value multiplied by the new supply, divided by the previous supply (`r * sp / s`). Effectively, it scales at the same rate as the supply.
The bug: if the decrease in supply of any given iteration of the solver is large enough, the constant product term can round down to zero. There is no revert to handle this case and once it occurs, each following iteration will remain zero since `0 * x / y = 0`.
Now that we have a zero constant product term, we no longer have a hybrid constant product/constant sum curve, instead we effectively just have a constant sum curve. To understand why this is a problem we have to go back and look at the curves. In the below graph [2], we have the intended curve (red) and the constant sum curve (purple) which is the result of the zero product term.
As we adjust the supply (see desmos graph [2] linked in reply) of these two curves (D), we can see that the reserves increase by the same amount in the middle, where the reserves are balanced, but by different amounts on the outside, where the reserves are imbalanced. This means that as we add/remove liquidity with imbalanced reserves, these two curves will mint/burn a different amount of LP tokens.
Understanding this behavior, the attacker systemically switched between these curves by triggering the zero constant product term when adding liquidity with unbalanced reserves to receive more LP tokens than intended. They then resolved the constant product term back to normal during liquidity removal to receive the correct amount of tokens provided for burning the inflated amount of LP tokens they received. This allowed the attacker to withdraw more tokens than they deposited, which they repeated until the pool was drained of its reserves for a profit of about ~$8m.
The Second Bug: Unexpected Underflow
You thought we were done? Nope, there's yet another bug that the attacker exploited to steal even more funds after already completely draining the pool.
Now that the pool is empty, and variables used for accounting are in such an unusual state, there is a significant side effect which occurs when we attempt to deposit certain dust amounts. Again, looking in the `_calc_supply` function, when we iteratively recompute the supply, we compute it with the following line (`(l - s * r) / d`):
Since we use unchecked math here and the accounting is in a highly irregular state, it's unexpectedly possible for `s * r > l`, resulting in the computed supply underflowing. The attacker exploits this underflow by depositing the following amounts: `[1, 1, 1, 1, 1, 1, 1, 9]`, resulting in them being minted `~2.6*10^56` yETH LP tokens. The attacker then makes a swap on the curve yETH/WETH pool, draining the pool of its WETH, for a profit of ~$1m.
Conclusion
Not only did this attack include a highly sophisticated AMM invariant exploit, but it also exploited an underflow which is likely only possible due to the existence of the invariant exploit. This combination of exploits allowed the attacker to not only drain the yETH pool, but also another pool containing the LP token. Both attacks, and even tornado cash deposits were all made in the same transaction, preventing any chance at rescue.
In my research, every writeup I came across misunderstood this attack in some way. Clearly, it's extremely rare to understand such a sophisticated exploit, providing for some serious alpha to developers and security researchers to fully wrap their heads around this.](https://pbs.twimg.com/media/G7V-9O0b0AEdU36.png)
![0xKaden's tweet photo. yETH Exploit Deep Dive
After spending some time exploring the recent yETH exploit, I quickly realized that it's easily one of the most sophisticated attacks I've ever seen. In fact, it was so complicated that every writeup I read misunderstood at least some part of the attack. This complexity provides for some serious alpha to developers and security researchers who can thoroughly understand the attack, so don't just bookmark this, let's dive in.
Hybrid AMM Curve
To understand this exploit, we first need to understand the underlying mechanism of the protocol. The yETH pool uses an invariant which is a hybrid between constant product and constant sum. If you're familiar with the inner workings of Uniswap, you should be familiar with the constant product behavior, essentially it just adjusts the price according to the reserves. Whereas constant sum results in a constant price between the tokens, regardless of reserves. The yETH hybrid curve behaves like a constant sum when the token reserves are balanced, keeping the price constant, and behaves like a constant product curve when the reserves are imbalanced. This behavior is valuable for pools of assets which have the same value due to the fact that the price is much less sensitive to reserve changes.
Below we have a graph [1] of these different curves. Red: constant product, green: constant sum, blue: hybrid used by the yETH pool.
The First Bug: Breaking The Invariant
Let's zoom in on the `_calc_supply` function. This function uses an iterative approximation to converge to a new supply and constant product term at each iteration, ending the loop once sufficient precision is achieved.
The constant product term (r) is recomputed at each iteration as the current value multiplied by the new supply, divided by the previous supply (`r * sp / s`). Effectively, it scales at the same rate as the supply.
The bug: if the decrease in supply of any given iteration of the solver is large enough, the constant product term can round down to zero. There is no revert to handle this case and once it occurs, each following iteration will remain zero since `0 * x / y = 0`.
Now that we have a zero constant product term, we no longer have a hybrid constant product/constant sum curve, instead we effectively just have a constant sum curve. To understand why this is a problem we have to go back and look at the curves. In the below graph [2], we have the intended curve (red) and the constant sum curve (purple) which is the result of the zero product term.
As we adjust the supply (see desmos graph [2] linked in reply) of these two curves (D), we can see that the reserves increase by the same amount in the middle, where the reserves are balanced, but by different amounts on the outside, where the reserves are imbalanced. This means that as we add/remove liquidity with imbalanced reserves, these two curves will mint/burn a different amount of LP tokens.
Understanding this behavior, the attacker systemically switched between these curves by triggering the zero constant product term when adding liquidity with unbalanced reserves to receive more LP tokens than intended. They then resolved the constant product term back to normal during liquidity removal to receive the correct amount of tokens provided for burning the inflated amount of LP tokens they received. This allowed the attacker to withdraw more tokens than they deposited, which they repeated until the pool was drained of its reserves for a profit of about ~$8m.
The Second Bug: Unexpected Underflow
You thought we were done? Nope, there's yet another bug that the attacker exploited to steal even more funds after already completely draining the pool.
Now that the pool is empty, and variables used for accounting are in such an unusual state, there is a significant side effect which occurs when we attempt to deposit certain dust amounts. Again, looking in the `_calc_supply` function, when we iteratively recompute the supply, we compute it with the following line (`(l - s * r) / d`):
Since we use unchecked math here and the accounting is in a highly irregular state, it's unexpectedly possible for `s * r > l`, resulting in the computed supply underflowing. The attacker exploits this underflow by depositing the following amounts: `[1, 1, 1, 1, 1, 1, 1, 9]`, resulting in them being minted `~2.6*10^56` yETH LP tokens. The attacker then makes a swap on the curve yETH/WETH pool, draining the pool of its WETH, for a profit of ~$1m.
Conclusion
Not only did this attack include a highly sophisticated AMM invariant exploit, but it also exploited an underflow which is likely only possible due to the existence of the invariant exploit. This combination of exploits allowed the attacker to not only drain the yETH pool, but also another pool containing the LP token. Both attacks, and even tornado cash deposits were all made in the same transaction, preventing any chance at rescue.
In my research, every writeup I came across misunderstood this attack in some way. Clearly, it's extremely rare to understand such a sophisticated exploit, providing for some serious alpha to developers and security researchers to fully wrap their heads around this.](https://pbs.twimg.com/media/G7V-uW8bsAAE--w.png)
![0xKaden's tweet photo. yETH Exploit Deep Dive
After spending some time exploring the recent yETH exploit, I quickly realized that it's easily one of the most sophisticated attacks I've ever seen. In fact, it was so complicated that every writeup I read misunderstood at least some part of the attack. This complexity provides for some serious alpha to developers and security researchers who can thoroughly understand the attack, so don't just bookmark this, let's dive in.
Hybrid AMM Curve
To understand this exploit, we first need to understand the underlying mechanism of the protocol. The yETH pool uses an invariant which is a hybrid between constant product and constant sum. If you're familiar with the inner workings of Uniswap, you should be familiar with the constant product behavior, essentially it just adjusts the price according to the reserves. Whereas constant sum results in a constant price between the tokens, regardless of reserves. The yETH hybrid curve behaves like a constant sum when the token reserves are balanced, keeping the price constant, and behaves like a constant product curve when the reserves are imbalanced. This behavior is valuable for pools of assets which have the same value due to the fact that the price is much less sensitive to reserve changes.
Below we have a graph [1] of these different curves. Red: constant product, green: constant sum, blue: hybrid used by the yETH pool.
The First Bug: Breaking The Invariant
Let's zoom in on the `_calc_supply` function. This function uses an iterative approximation to converge to a new supply and constant product term at each iteration, ending the loop once sufficient precision is achieved.
The constant product term (r) is recomputed at each iteration as the current value multiplied by the new supply, divided by the previous supply (`r * sp / s`). Effectively, it scales at the same rate as the supply.
The bug: if the decrease in supply of any given iteration of the solver is large enough, the constant product term can round down to zero. There is no revert to handle this case and once it occurs, each following iteration will remain zero since `0 * x / y = 0`.
Now that we have a zero constant product term, we no longer have a hybrid constant product/constant sum curve, instead we effectively just have a constant sum curve. To understand why this is a problem we have to go back and look at the curves. In the below graph [2], we have the intended curve (red) and the constant sum curve (purple) which is the result of the zero product term.
As we adjust the supply (see desmos graph [2] linked in reply) of these two curves (D), we can see that the reserves increase by the same amount in the middle, where the reserves are balanced, but by different amounts on the outside, where the reserves are imbalanced. This means that as we add/remove liquidity with imbalanced reserves, these two curves will mint/burn a different amount of LP tokens.
Understanding this behavior, the attacker systemically switched between these curves by triggering the zero constant product term when adding liquidity with unbalanced reserves to receive more LP tokens than intended. They then resolved the constant product term back to normal during liquidity removal to receive the correct amount of tokens provided for burning the inflated amount of LP tokens they received. This allowed the attacker to withdraw more tokens than they deposited, which they repeated until the pool was drained of its reserves for a profit of about ~$8m.
The Second Bug: Unexpected Underflow
You thought we were done? Nope, there's yet another bug that the attacker exploited to steal even more funds after already completely draining the pool.
Now that the pool is empty, and variables used for accounting are in such an unusual state, there is a significant side effect which occurs when we attempt to deposit certain dust amounts. Again, looking in the `_calc_supply` function, when we iteratively recompute the supply, we compute it with the following line (`(l - s * r) / d`):
Since we use unchecked math here and the accounting is in a highly irregular state, it's unexpectedly possible for `s * r > l`, resulting in the computed supply underflowing. The attacker exploits this underflow by depositing the following amounts: `[1, 1, 1, 1, 1, 1, 1, 9]`, resulting in them being minted `~2.6*10^56` yETH LP tokens. The attacker then makes a swap on the curve yETH/WETH pool, draining the pool of its WETH, for a profit of ~$1m.
Conclusion
Not only did this attack include a highly sophisticated AMM invariant exploit, but it also exploited an underflow which is likely only possible due to the existence of the invariant exploit. This combination of exploits allowed the attacker to not only drain the yETH pool, but also another pool containing the LP token. Both attacks, and even tornado cash deposits were all made in the same transaction, preventing any chance at rescue.
In my research, every writeup I came across misunderstood this attack in some way. Clearly, it's extremely rare to understand such a sophisticated exploit, providing for some serious alpha to developers and security researchers to fully wrap their heads around this.](https://pbs.twimg.com/media/G7V-KNcb0AA9XDy.png)
Olivia
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It took a little longer than expected, but we have created a website for people to view the footage collected from Gaza in one place. You no longer have to download the entire archives to see them.
It includes:
64,537 videos
17,905 photos
Ability to download individual videos
Searchable index
Exhaustive sources list (300+ journalists)
Geolocation data
Livemap with minute to minute updates
Victim list
It can be accessed here: https://t.co/s0Se94PXWF
Please share & quote tweet to help this post break out of the twitter algorithm prison.
We will keep adding the rest of the archives to the site, be patient- it is difficult work. Continue to seed the torrents provided, as that is the best way to ensure the footage remains stored in decentalized way.
God bless all those who sacrificed their lives to get this footage out, and everyone invovled in collecting/archiving it.
Join our telegram:
https://t.co/bvcis3b9GT
Follow our backup accounts:
@ZionismExposedx & @IsraelExposedAr
4步教你复活 claude fable 5！
1.下载 fable 5的系统提示词
https://t.co/FFk7VdR0vO
2.放到你Claude code 项目文件夹
3. 使用启动命令 claude --dangerously-skip-permissions --system-prompt-file CLAUDE-FABLE-5.md
4.模型切换到 opus 4.8 Max
只需要这4步
你会发现 fable 5 又回来了！
Dear working class, let's not fight each other. Those earning 15k, makan gaji already paid taxes. They can complain and influence policy making. You know who we should fight? Politicians and their cronies who keep enriching each other whose income come from us, with ZERO yield!
@d3f7ult Salam bhaiya, can i dm you?
China rural population was going through malnutrition and growth stunting. They increased the 3 yuan to 5 yuan per student per day and made it mandatory for the government officials to eat the same meal as what those kids were eating. They added more meat, more milk, allowed more eggs and tofu for the kids in everyday meal.
Result? 13-year-old males have seen the most growth in height and weight over the decade, with an average height of 7.5 centimeters taller and an average added weight of 6.6 kilograms from 2012 to 2021.
12-year-old female students who experienced the most growth had added an average of 6.3 cm in height and 5.8 kg in weight in 2021 compared to average levels by 2012.
This is what happens when you keep your schools separate from your religious identities and don't do drama and rr on allowing eggs or meat or chicken in the mid day meals.
(now a certain X users with low attention span will take this post as 'oh so you are saying veg food has no protein wow')
Read it again.
@infinitelogins RESOURCES
Learning from past audits shouldn’t require hours of context.
We’re building a new tool that lets you learn faster from real audit reports, jump straight into code, bugs, and PoCs, and actually understand why issues exist.
From protocol walkthroughs (like Morpho) to real vulnerability patterns all in one flow.
If you’re trying to break into Web3 security, this is for you.
DM me and let’s talk onboarding.
Don’t look away.
This was a Tapanuli orangutan, the world’s rarest great ape.
Its decayed body was recently found among logging debris and mud, a consequence of extreme weather exacerbated by large-scale deforestation in North Sumatra. Up to 54 members of its family might also have been killed.
https://t.co/zIoCz7NGkU
An enormously fat polar bear that has been gorging itself on the carcass of a whale on the coast of Norway.
yETH Exploit Deep Dive
After spending some time exploring the recent yETH exploit, I quickly realized that it's easily one of the most sophisticated attacks I've ever seen. In fact, it was so complicated that every writeup I read misunderstood at least some part of the attack. This complexity provides for some serious alpha to developers and security researchers who can thoroughly understand the attack, so don't just bookmark this, let's dive in.
Hybrid AMM Curve
To understand this exploit, we first need to understand the underlying mechanism of the protocol. The yETH pool uses an invariant which is a hybrid between constant product and constant sum. If you're familiar with the inner workings of Uniswap, you should be familiar with the constant product behavior, essentially it just adjusts the price according to the reserves. Whereas constant sum results in a constant price between the tokens, regardless of reserves. The yETH hybrid curve behaves like a constant sum when the token reserves are balanced, keeping the price constant, and behaves like a constant product curve when the reserves are imbalanced. This behavior is valuable for pools of assets which have the same value due to the fact that the price is much less sensitive to reserve changes.
Below we have a graph [1] of these different curves. Red: constant product, green: constant sum, blue: hybrid used by the yETH pool.
The First Bug: Breaking The Invariant
Let's zoom in on the `_calc_supply` function. This function uses an iterative approximation to converge to a new supply and constant product term at each iteration, ending the loop once sufficient precision is achieved.
The constant product term (r) is recomputed at each iteration as the current value multiplied by the new supply, divided by the previous supply (`r * sp / s`). Effectively, it scales at the same rate as the supply.
The bug: if the decrease in supply of any given iteration of the solver is large enough, the constant product term can round down to zero. There is no revert to handle this case and once it occurs, each following iteration will remain zero since `0 * x / y = 0`.
Now that we have a zero constant product term, we no longer have a hybrid constant product/constant sum curve, instead we effectively just have a constant sum curve. To understand why this is a problem we have to go back and look at the curves. In the below graph [2], we have the intended curve (red) and the constant sum curve (purple) which is the result of the zero product term.
As we adjust the supply (see desmos graph [2] linked in reply) of these two curves (D), we can see that the reserves increase by the same amount in the middle, where the reserves are balanced, but by different amounts on the outside, where the reserves are imbalanced. This means that as we add/remove liquidity with imbalanced reserves, these two curves will mint/burn a different amount of LP tokens.
Understanding this behavior, the attacker systemically switched between these curves by triggering the zero constant product term when adding liquidity with unbalanced reserves to receive more LP tokens than intended. They then resolved the constant product term back to normal during liquidity removal to receive the correct amount of tokens provided for burning the inflated amount of LP tokens they received. This allowed the attacker to withdraw more tokens than they deposited, which they repeated until the pool was drained of its reserves for a profit of about ~$8m.
The Second Bug: Unexpected Underflow
You thought we were done? Nope, there's yet another bug that the attacker exploited to steal even more funds after already completely draining the pool.
Now that the pool is empty, and variables used for accounting are in such an unusual state, there is a significant side effect which occurs when we attempt to deposit certain dust amounts. Again, looking in the `_calc_supply` function, when we iteratively recompute the supply, we compute it with the following line (`(l - s * r) / d`):
Since we use unchecked math here and the accounting is in a highly irregular state, it's unexpectedly possible for `s * r > l`, resulting in the computed supply underflowing. The attacker exploits this underflow by depositing the following amounts: `[1, 1, 1, 1, 1, 1, 1, 9]`, resulting in them being minted `~2.6*10^56` yETH LP tokens. The attacker then makes a swap on the curve yETH/WETH pool, draining the pool of its WETH, for a profit of ~$1m.
Conclusion
Not only did this attack include a highly sophisticated AMM invariant exploit, but it also exploited an underflow which is likely only possible due to the existence of the invariant exploit. This combination of exploits allowed the attacker to not only drain the yETH pool, but also another pool containing the LP token. Both attacks, and even tornado cash deposits were all made in the same transaction, preventing any chance at rescue.
In my research, every writeup I came across misunderstood this attack in some way. Clearly, it's extremely rare to understand such a sophisticated exploit, providing for some serious alpha to developers and security researchers to fully wrap their heads around this.
![0xKaden's tweet photo. yETH Exploit Deep Dive
After spending some time exploring the recent yETH exploit, I quickly realized that it's easily one of the most sophisticated attacks I've ever seen. In fact, it was so complicated that every writeup I read misunderstood at least some part of the attack. This complexity provides for some serious alpha to developers and security researchers who can thoroughly understand the attack, so don't just bookmark this, let's dive in.
Hybrid AMM Curve
To understand this exploit, we first need to understand the underlying mechanism of the protocol. The yETH pool uses an invariant which is a hybrid between constant product and constant sum. If you're familiar with the inner workings of Uniswap, you should be familiar with the constant product behavior, essentially it just adjusts the price according to the reserves. Whereas constant sum results in a constant price between the tokens, regardless of reserves. The yETH hybrid curve behaves like a constant sum when the token reserves are balanced, keeping the price constant, and behaves like a constant product curve when the reserves are imbalanced. This behavior is valuable for pools of assets which have the same value due to the fact that the price is much less sensitive to reserve changes.
Below we have a graph [1] of these different curves. Red: constant product, green: constant sum, blue: hybrid used by the yETH pool.
The First Bug: Breaking The Invariant
Let's zoom in on the `_calc_supply` function. This function uses an iterative approximation to converge to a new supply and constant product term at each iteration, ending the loop once sufficient precision is achieved.
The constant product term (r) is recomputed at each iteration as the current value multiplied by the new supply, divided by the previous supply (`r * sp / s`). Effectively, it scales at the same rate as the supply.
The bug: if the decrease in supply of any given iteration of the solver is large enough, the constant product term can round down to zero. There is no revert to handle this case and once it occurs, each following iteration will remain zero since `0 * x / y = 0`.
Now that we have a zero constant product term, we no longer have a hybrid constant product/constant sum curve, instead we effectively just have a constant sum curve. To understand why this is a problem we have to go back and look at the curves. In the below graph [2], we have the intended curve (red) and the constant sum curve (purple) which is the result of the zero product term.
As we adjust the supply (see desmos graph [2] linked in reply) of these two curves (D), we can see that the reserves increase by the same amount in the middle, where the reserves are balanced, but by different amounts on the outside, where the reserves are imbalanced. This means that as we add/remove liquidity with imbalanced reserves, these two curves will mint/burn a different amount of LP tokens.
Understanding this behavior, the attacker systemically switched between these curves by triggering the zero constant product term when adding liquidity with unbalanced reserves to receive more LP tokens than intended. They then resolved the constant product term back to normal during liquidity removal to receive the correct amount of tokens provided for burning the inflated amount of LP tokens they received. This allowed the attacker to withdraw more tokens than they deposited, which they repeated until the pool was drained of its reserves for a profit of about ~$8m.
The Second Bug: Unexpected Underflow
You thought we were done? Nope, there's yet another bug that the attacker exploited to steal even more funds after already completely draining the pool.
Now that the pool is empty, and variables used for accounting are in such an unusual state, there is a significant side effect which occurs when we attempt to deposit certain dust amounts. Again, looking in the `_calc_supply` function, when we iteratively recompute the supply, we compute it with the following line (`(l - s * r) / d`):
Since we use unchecked math here and the accounting is in a highly irregular state, it's unexpectedly possible for `s * r > l`, resulting in the computed supply underflowing. The attacker exploits this underflow by depositing the following amounts: `[1, 1, 1, 1, 1, 1, 1, 9]`, resulting in them being minted `~2.6*10^56` yETH LP tokens. The attacker then makes a swap on the curve yETH/WETH pool, draining the pool of its WETH, for a profit of ~$1m.
Conclusion
Not only did this attack include a highly sophisticated AMM invariant exploit, but it also exploited an underflow which is likely only possible due to the existence of the invariant exploit. This combination of exploits allowed the attacker to not only drain the yETH pool, but also another pool containing the LP token. Both attacks, and even tornado cash deposits were all made in the same transaction, preventing any chance at rescue.
In my research, every writeup I came across misunderstood this attack in some way. Clearly, it's extremely rare to understand such a sophisticated exploit, providing for some serious alpha to developers and security researchers to fully wrap their heads around this.](https://pbs.twimg.com/media/G7V_LG3bcAAmYBP.png)
@Rainmaker1973 Aint that how a gear transmission work?
A car-less world in Malaysia is possible.
in PJ, 150,000+ residents live along Sungai Penchala and Sungai Kayu Ara.
what if we built bike lanes along the river, connecting you from your condo to Lembah Subang LRT, or Bandar Utama MRT?
Hello, my name is Ufuk. I’m 28 years old and Turkish. I’m 97% disabled. I’m paralyzed from the neck down and unable to use my hands. For almost three months, I’ve been posting tweets and making videos about @SentientAGI. I’ve created many tweets and videos, and it has been extremely difficult since I can’t use my fingers. Even while in the hospital, I posted tweets and videos every day. I’m writing this from the hospital right now.
Because of my condition, I can’t be active on Discord or type messages there. Writing exhausts me, which is why I focused on tweeting and creating videos instead. @scalpkripto is aware of my situation. Recently, you’ve done special things for the Turkish community. I only have the Early AGI role, but I’ve put in more effort than many others.
My financial situation isn’t good, and I rely on what I create to support myself. I’m sharing my updated portfolio link with you, and I kindly ask you to take another look. Thank you. 🙏 @shad_haq_ @namyura_ @qzxcle @vivekkolli @oleg_golev @sandeepnailwal
Portfolio link: https://t.co/jEYUJJP2ff
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