Formal automatic verification of authentication cryptographic protocols
Bibliographic record
Abstract
We address the formal analysis of authentication cryptographic protocols. We present a new verification algorithm that generates from the protocol description the set of possible flaws, if any, as well as the corresponding attack scenarios. This algorithm does not require any property or invariant specification. The algorithm involves three steps: extracting the protocol roles, modeling the intruder abilities and verification. In addition to the classical known intruder computational abilities such as encryption and decryption, we also consider those computations that result from different instrumentations of the protocol. The intruder abilities are modeled as a deductive system. The verification is based on the extracted roles as well as the deductive system. It consists in checking whether the intruder can answer all the challenges uttered by a particular role. If it is the case, an attack scenario is automatically constructed. The extracted proof system does not ensure the termination of deductions. For that purpose, we present a general transformation schema that allows one to automatically rewrite the non-terminating proof system into a terminating one. The transformation schema is shown to be correct. To exemplify the usefulness and efficiency of our approach, we illustrate it on the Woo and Lam (1992) authentication protocol. Abadi and Needham have shown that the protocol is insecure and they proposed a new corrected version. Thanks to this method we have discovered new unknown flaws in the Woo and Lam protocol and in the corrected version of Abadi and Needham.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".