A Lightweight Type System with Uniqueness and Typestates for the Java Cryptography API
Bibliographic record
Abstract
Java cryptographic APIs facilitate building secure applications, but not all developers have strong cryptographic knowledge to use these APIs correctly. \nSeveral studies have shown that misuses of those cryptographic APIs may cause significant security vulnerabilities, compromising the integrity of applications and exposing sensitive data. Hence, \nit is an important problem to design methodologies and techniques, which can guide developers in building secure applications with minimum effort, and that are accessible to non-experts in cryptography. \nIn this thesis, we present a methodology that reasons about the correct usage of Java cryptographic APIs with types, specifically targeting to cryptographic applications. \nOur type system combines aliasing control and the abstraction of object states into typestates, allowing users to express a set of user-defined disciplines on the use of cryptographic APIs and invariants on variable usage. More specifically, we employ the typestate automaton to depict typestates within our type system, and we control aliases by applying the principle of uniqueness to sensitive data. \n \n \nWe mainly focus on the usage of initialization vectors. An initialization vector is a binary vector used as the input to initialize the state for the encryption of a plaintext block sequence. Randomization and uniqueness are crucial to an initialization vector. Failing to maintain a unique initialization vector for encryption can compromise confidentiality. Encrypting the same plaintext with the same initialization vector always yields the same ciphertext, thereby simplifying the attacker's task of guessing the cipher pattern. \n \nTo address this problem practically, we implement our approach as a pluggable type system on top of the EISOP Checker Framework. \nTo minimize the cryptographic expertise required by application developers looking to incorporate secure computing concepts into their software, our approach allows cryptographic experts to plug in the protocols into the system. \nIn this setting, developers merely need to provide minimal annotations on sensitive data—requiring little cryptographic knowledge. \n \nWe also evaluated our work by performing experiments over one benchmark and 7 real-world Java projects from Github. We found that 6 out 7 projects have security issues. In summary, we found 12 misuses in initialization vectors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".