Is Ajna Protocol Safe?
Risk Grade: B- (31/100)
Ajna Protocol is rated as moderate risk — some novel mechanisms, generally well-understood.
Moderate risk — innovative oracle-free lending with immutable code, but unfixable bugs and open pool creation create trust-at-your-own-risk dynamics
A lending platform where anyone can create a market for any token without relying on external price feeds. It holds $602K in deposits and operates with completely immutable code and zero governance. Its B- grade reflects genuinely innovative oracle-free design and small scale, offset by the manipulation risks inherent in internal price discovery and the inability to patch any bugs found after launch.
TVL
$574,331
Mechanisms
6
Interactions
5
Value Grade
C-
Key Risks for Ajna Protocol Users
Without external price feeds, attackers can inflate a token's value inside a lending pool, borrow real money against it, then crash the token elsewhere. Lenders are left holding worthless collateral.
Anyone can create a lending pool for any token, including scam tokens. There is no review process or safety checks.
The protocol's code can never be changed. If a critical bug is found, the team can only warn users. They cannot fix it.
Top Risk Factors
- •Oracle-free price discovery creates manipulation risk where attackers can inflate collateral values in isolated pools, borrow against overvalued assets, then crash prices externally, leaving lenders with bad debt.
- •Immutable code with no governance means critical bugs or economic exploits cannot be patched. The protocol must live with any discovered vulnerabilities permanently.
- •Permissionless pool creation allows anyone to create markets for manipulable or scam tokens without protocol-level risk assessment.
Risk Score Breakdown
Ajna Protocol's highest risk area is Mechanism Novelty (9/15). Here's how each dimension contributes to the overall 31/100 score:
Read the Full Ajna Protocol Risk Report
This protocol has 2 collapse scenarios. 1 high-severity interaction risks identified. See the full mechanism classification, interaction matrix, and deep-dive recommendations.
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