Phishing has evolved. In the early days of the internet, it was a trawling exercise. It involved imprecisely casting a wide net of from a supposed wealthy prince, a bank or a lottery with emails hoping to catch anyone in an information-poor environment.
Today, attacker’s nets have been replaced by much more precise tools and strategies that take advantage of the scale of widely available public information. On a public blockchain, your financial history is a map of your online behaviour that may be visible to everyone.
Midnight’s privacy-enhancing technology does more than just hide balances. It removes the data required to build the digital weapons used for attacks. By decoupling your identity from the public transaction graph, Midnight provides a structural security advantage that many users may not even be aware of.
To understand the role shielded assets play in preventing these attacks, it is helpful to understand how attackers use the information on a transparent ledger, and a bit more about shielded assets.
Data harvesting and whale watching
One of the most basic issues with a public ledger is that it makes it easy to identify potential targets.
On a public chain, the ledger acts as a real-time, open-source database for target acquisition by malicious actors. Attackers use automated scripts to monitor the blockchain for large transfers or high-balance addresses. Because every asset is linked to a public key, an attacker can create a comprehensive profile of a potential victim, including information like:
- Total Net Worth: The sum of all tokens and NFTs associated with an address.
- Asset Composition: The specific identifiers of high-value notes or tokens.
- Interaction History: The DApps the user frequently interacts with, which informs the choice of a phishing lure.
- Behavioral Patterns: When the user is typically active, allowing for attacks timed when the victim could be more vulnerable, distracted or tired.
Shielded assets on Midnight keep this data private.
The malicious transaction construction
The transition from observing a target to executing a theft relies on a key variable: data availability. On a public blockchain, harvested information provides the exact details required to build a fraudulent request.
On-chain behavioral tracking allows attackers to time these interventions with high precision. For instance, if a user regularly transfers tokens from a centralized exchange at a set time, an attacker can monitor the ledger for that specific activity. The attacker then sends a spoofed email claiming a transaction error or a security flag, directing the user to a malicious portal to resolve the issue.
When the lure matches a recent, real-world action, the phishing attempt carries a high degree of perceived legitimacy.
This is called ice phishing, a method where an attacker tricks a user into signing a pre-filled transaction. The attacker identifies every unspent transaction output (UTXO) or specific token identifiers associated with the address.
Users often lack the technical expertise to decode raw data within a wallet signature request. It looks convincing, so the user signs without seeing the exploit. Spoofed sites mimic legitimate protocols to gain trust during the interaction. The user remains unaware that the signature authorizes a custom instruction to transfer their specific holdings to an unauthorized wallet.
Shielded assets create structural defences
Shielded assets on Midnight can break this cycle by ensuring the data required to build these lures is never recorded on the blockchain. If the attacker cannot see your behavioral patterns, they cannot even begin to plan or time the strike.
This essentially puts attackers back to the wealthy prince era of unspecified and unconvincing attacks. Without a map of your behavioral graph, phishing returns to being a "trawling" exercise which is imprecise, unconvincing, and easily ignored. Attackers cannot weaponize what they cannot find.
Up next
Preventing target selection is only the first layer of defense. The next article examines the cryptographic mechanics beneath the surface, exploring how blinding factors, viewing keys, zero-knowledge proofs, and nullifiers provide additional layers of protection for users.