Bitcoin is the cryptocurrency that everyone has heard about, but it is only one kind of cryptocurrency. There are many others, and some of them work in different ways. One of the ones that has always interested me is Zcash. In my book “The Currency Cold War” I used it to illustrate some points about privacy, because Zcash was specifically built to offer something Bitcoin does not: privacy. It uses clever cryptography (in the form of zero-knowledge proofs) to provide on‑chain privacy for people who want their balances and transaction details kept confidential. Users can move coins into “shielded pools” where addresses and amounts are hidden, but every transaction comes with a cryptographic proof that no money has been created or destroyed and that the spender is authorised
Orchard is the most recent of these pools and unfortunately it tuirned out to have a bug. In simple terms, the mathematical “circuit” that checked whether a private transaction in Orchard was valid had a subtle error in it, means that an attacker could construct a transaction that passed all the network’s checks while quietly creating new digital currency out of thin air.
(Note that the zero‑knowledge proofs worked correctly, the problem was to do with what the circuit was asking the proofs to certify. )
Now, as I am sure you are aware, bugs are discovered in the crypto world all the time. I looked at last year for a few examples. There were plenty of them, so here’s three of them to get you thinking. In February, Bybit lost about $1.4 billion after attackers tricked signers in its wallet workflow and redirected funds, process failure around approvals and signing infrastructure. For non-specialists, think of it like a bank where the vault is intact, but the people and systems authorizing transfers are manipulated. That makes operational security, approval checks, and key management just as important as the code itself. In May, the Cetus Protocol was exploited for more than $200 million because of a mathematical flaw in liquidity calculations whereby an attacker found a way to make the protocol misread numbers and treat an impossible situation as valid. In November Balancer v2 pools were exploited through a smart-contract access-control problem combined with a rounding/invariant manipulation issue that led to more than $100 million in losses when a hacker found a way to behave as if they had rights they did not really have and then used tiny calculation weaknesses to tilt the system in their favor.
OK, someone found another bug. No big deal. But this bug is particulary interesting because of the privacy angle. Since Orchard hides balances and flows, the bogus currency created out of thin air was effectively undetectable. In systems like Bitcoin, an arbitrary increase in supply would be obvious just by counting coin but in Orchard, the ledger is consistent by construction; the only signal that something is wrong would be an unexplained swell of coins leaving the shielded pool. A researcher built a proof‑of‑concept, showed that unlimited counterfeit ZEC could in principle be minted inside Orchard, and reported this privately to the Zcash developers. They coordinated an emergency patch and network upgrade, then disclosed the issue publicly a few days later. There is no public evidence that anyone exploited the flaw in the wild, but equally, the privacy design makes it impossible to prove with certainty that nobody ever did.
For the broader cryptocurrency sector, the incident is am interesting comment on the trade‑off between privacy and auditability. With Bitcoin, any node can verify the “money ski supply, track coin flows, and check the rules directly against what it sees on‑chain. As systems become more private and more complex, the burden shifts: the community must trust that the circuits and smart contracts faithfully encode the intended rules, because the chain itself no longer offers a simple, human‑readable audit trail. A single logic mistake in a zero‑knowledge circuit or a privacy‑preserving protocol can compromise the monetary integrity of the entire system without leaving obvious fingerprints.
What caught my eye though is *how* the vulnerability was found. The researcher used a state‑of‑the‑art AI model as an interactive co‑analyst. In practice, that meant feeding it protocol descriptions, code fragments and constraints then iteratively asking it to reason about vulnerabilities and then construct a concrete attack that satisfied all the apparent rules while still breaking conservation of value. The human attacker framed the questions and judged the answers, but the model accelerated the search through a very large and subtle design space.
This points to a step‑change in how protocol risk will be managed. Historically, the limiting resource in cryptographic protocol auditing has been expert attention: there are only so many people on the planet who can read a zero‑knowledge circuit or a complex smart‑contract system and spot the one edge case where something goes wrong. AI‑assisted analysis effectively multiplies that expertise. It can explore many more paths, propose candidate counterexamples, and cross‑check assumptions far faster than a human alone. That is exactly what happened with Orchard: a bug that had survived years of expert scrutiny surfaced once a human used AI as a force‑multiplier on their reasoning process.
This capability is available to all attackers. An adversary can point powerful models at public code and specifications, ask them to “find a way to break value conservation” or “construct a transaction that passes all checks but increases balance,” and iterate until something breaks. For protocols handling serious value and/or implementing serious privacy, AI will need to be to part of the defence: theorem theorem‑checking around critical invariants, continuous probing guided by models and formal specifications that models can reason against. This means that projects that invest early in AI‑assisted formal verification and red‑teaming will be much better placed to withstand the kind of scrutiny that the attackers will bring to the game.
For investors, exchanges, and regulators, the message is clear. Privacy coins and other sophisticated protocols can offer powerful features, but they also carry more complex failure modes. Monetary integrity is binary: either a system can credibly claim that its supply rules are unbreakable, or its long‑term value proposition is weakened. Going forward, projects will be judged not just on innovation but on the quality of their engineering governance: independent audits, formal methods, robust incident response, and transparent communication when things go wrong.