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Vulnerabilities
jackson-databind: quadratic forward-reference completion in Collection and Map deserializers
What is it?
Forward-reference completion for @JsonIdentityInfo object IDs in FasterXML jackson-databind performs a linear scan of the pending-reference accumulator for every resolved ID. The affected paths are CollectionDeserializer.CollectionReferringAccumulator.resolveForwardReference() and the equivalent implementation in MapDeserializer. When a document first creates N unresolved object-ID references in an identity-enabled collection or map and then defines those same IDs in reverse order, completion performs on the order of N * (N + 1) / 2 identity comparisons, so a shallow document whose size grows linearly causes quadratic CPU work during deserialization. The reporter instrumented equals() calls on the ID class and measured exactly 2,003,000 comparisons at N = 2,000, against zero comparisons in the pending-reference lookup path for an equally sized control in which every reference was already resolved. The input requires no deep nesting and no syntactically unusual JSON. Exploitation requires an application that deserializes attacker-influenced JSON into an identity-enabled collection or map. The fix replaces the repeated linear lookup with a keyed pending-reference structure.
Evidence
No coverage recorded yet.
Exploitation
- Public proof of concept
- Exploited in the wild
- Listed as known exploited
- CVSS
- 7.5
- EPSS
- 0.4%
- First seen
- 7 d ago
- Status
- corroborated
Sources
CERTs and cyber security agencies, national news outlets in every country we could find, vendor advisories, research blogs and the security press. Every source is polled on its own schedule, by RSS where possible and by headless browser where not. Broken feeds are repaired automatically; anything still failing is listed here.
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Pipeline, last 24 hours
How many articles each type of source produced, how many were read in full, and how many were linked to a vulnerability. “Unmatched” are articles that look like vulnerability news but couldn’t be linked; the reasons and examples show what to improve.
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How it works
Vulnocalypse reads vulnerability registries, government CERTs, vendor advisories, research blogs and newsrooms around the clock, links everything that refers to the same vulnerability, and only shows what passes automated validation.
What gets into the feed
A vulnerability is admitted when it is corroborated, either through a published CVE record or through at least two independent organisations, and its impact score reaches 60 out of 100. Anything listed in a known-exploited catalogue is admitted regardless of score. Once admitted, an entry stays, so the feed never flickers.
Impact score
Up to 30 points for severity (CVSS, or inferred from the description when no score exists), 25 for exploitation, 15 for blast radius by product category (edge devices and identity systems score highest), 15 for attention from news, CERTs and researchers, 10 for EPSS exploitation probability, and 5 for pre-authentication. Every entry shows its breakdown.
Exploitation status
We only mark something exploited when there is a citable source: a KEV listing (CISA, VulnCheck or ENISA), a statement from the vendor or a government agency, CISA’s SSVC assessment, or at least two independent organisations reporting attacks with no later denial from the vendor. Phrases like “no evidence of exploitation” are detected and count against a claim.
Validation, without humans
Identifiers are checked against the CVE List; rejected or non-existent CVEs are dropped. Titles, vendors, products and CVSS come from the official record rather than from headlines. Wire stories syndicated across outlets are counted once. Roundups that list dozens of CVEs only count the ones they actually discuss. Reports without an identifier wait for corroboration and are merged into the right CVE once one is assigned. Every decision is logged and shown on each vulnerability’s page.
The Panic meter
Three numbers over the past 24 hours, counting only vulnerabilities first seen in the last 14 days: distinct researchers posting about them, distinct news organisations covering them, and distinct CERTs and agencies warning about them. Each is ranked against its own 90-day history, weighted 35% CERTs, 35% news and 30% researchers, and stretched so the top levels stay rare. Each day’s score is split across the vulnerabilities that caused it.
The outbreak map
The map shows two things for every country: how many national news outlets are covering new vulnerabilities, and whether the national CERT or cyber security agency has issued a warning. National outlets are read directly, through local-language Google News editions, and through GDELT, which attributes coverage in over 100 languages to its source country. International security press is left off the map so it shows national attention, not trade coverage. Replay the spread to watch a vulnerability travel from patient zero across the world.
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