From protocol specifications to flaws and attack scenarios: an automatic and formal algorithm
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Presents a new approach to the verification of authentication protocols. This approach is formal, fully automatic and does not necessitate any specification of any protocol property or invariant. It takes the protocol specification as the parameter and generates the set of flaws, if any, as well as the corresponding attack scenarios. This approach involves three steps. First, protocol roles are extracted from the protocol specification. Second, the intruder's abilities to perform communication and computation are generated from the protocol specification. 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's abilities are modeled as a deductive system. Third, the extracted roles as well as the deductive system are combined to perform the verification. The latter consists in checking whether the intruder can answer all the challenges uttered by a particular role. If that is the case, an attack scenario is automatically constructed. To exemplify the usefulness and efficiency of our approach, we illustrate it on the Woo and Lam (1994) authentication protocol.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it