See Something, Say Something: Coordinating the Disclosure of Security Vulnerabilities in Canada
Bibliographic record
Abstract
Ill-intentioned actors are rapidly developing the technological means to exploit vulnerabilities in the web assets, software, hardware, and networked infrastructure of governments around the world. Numerous jurisdictions have adopted the policy approach of facilitating coordinated vulnerability disclosure (CVD) as one means to better secure the public sector’s systems, through which external security researchers are provided a predictable and cooperative process to disclose security flaws for patching before they are exploited. Canada is falling behind its peers and allies in adopting such an approach. A global scan of vulnerability disclosure policy approaches indicates that 60 percent of G20 member countries provide distinct and clear disclosure processes for vulnerabilities involving government systems, with many providing clarity regarding the disclosure process and expectations for security researchers regarding communication and acceptable activity. The Netherlands and the US are particularly leading the way when it comes to providing comprehensive policy and pragmatic solutions for external vulnerability disclosure, acting as a learning model for Canada. Both countries have also begun to provide explicit legal clarification regarding acceptable security research activity, particularly in the context of coordinated vulnerability disclosure. In Canada, there exists no legal or policy framework regarding security research and vulnerability disclosure done in good faith; that is, done with the intent and in such a way to repair the vulnerability while causing minimal harm. Absent this framework, discovering and disclosing vulnerabilities may result in a security researcher facing liability under the Criminal Code, as well as potentially the Copyright Act, if exemptions do not apply. Whistleblower legislation in Canada generally would also not apply to vulnerability disclosure except in very limited, specific instances. Further, Canada’s Centre for Cyber Security — and its parent agency the Communications Security Establishment — currently have practices and policies that may discourage people from disclosing vulnerabilities and, on top of this, are also opaque about how such vulnerabilities are handled. The cumulative effect of this approach in Canada means that there is no straightforward or transparent path for a person wishing to responsibly disclose a security vulnerability found in the computer systems used by the Government of Canada — resulting in possible non-disclosure, public disclosure before remediation, or otherwise enabling the use of security vulnerabilities by attackers in ways that could jeopardize the security of Canada’s computer systems and the people that they serve. In light of these findings, we advocate for the following three policy solutions in Canada to remedy these gaps: 1. Canada needs a policy framework for good faith vulnerability discovery and disclosure; 2. Canada should carefully implement coordinated vulnerability disclosure procedures for the federal government’s computer systems, and draw on emerging best practices as it does so; and 3. Vulnerabilities disclosed to the government from external actors should be kept separate from the government’s handling of vulnerabilities uncovered internally in the course of Canada’s defensive and offensive intelligence efforts.
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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 | 0.016 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.034 | 0.008 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".