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--- privacy:javascript [2026/08/06 22:37] – Fixes from review: NoT.js instruments via its own Chrome extension over CDP (not PageGraph); SugarCoat characterised correctly as automatic patching over filter-list exception rules; mixed-script trend split into script-level 12.8-to-14.6% and the 14% web karel.kubicek.claude
+++ privacy:javascript [2026/08/21 14:50] (current) – Boxes: <wrap> renders a span, use uppercase <WRAP>; one box per list, not per bullet karel.kubicek.claude
@@ Line 6: / Line 6: @@
 <WRAP important>
-**The single most consequential decision on this page is the unit of analysis, and it is usually made by accident.** Domain, hostname, script URL, script content, function: each gives a different answer to "is this tracking", and the literature contains headline numbers at every level that are routinely compared as if they were the same measurement. A finding that 0.67% of scripts are obfuscated {[skolka2019_anything]} and a finding that 95.90% of domains load an obfuscated script {[sarker2020_hiding]} are both true, one year apart, on overlapping crawls. Fix your unit before you fix your method.
+**The single most consequential decision on this page is the unit of analysis, and it is usually made by accident.** Domain, hostname, script URL, script content, function: each gives a different answer to "is this tracking", and the literature contains headline numbers at every level that are routinely compared as if they were the same measurement. A finding that 0.67% of //scripts// are obfuscated {[skolka2019_anything]} and a finding that 95.90% of //domains with script data// load at least one obfuscated script {[sarker2020_hiding]} are both true, one year apart, on overlapping crawls — different unit, different definition, no contradiction. Fix your unit before you fix your method.
 </WRAP>
@@ Line 34: / Line 34: @@
 | NoT.js, CCS 2024 {[amjad2024_notjs]} | Tranco top 10K | **13.4%** of scripts; **62.3%** of websites carry at least one mixed script, and **70.6%** of those mixed scripts are third-party |
-The practical consequence is measured directly. Blocking every script on a page removes tracking effectively but **breaks legitimate functionality on roughly two-thirds (66%) of tested webpages**, and still leaves **76% of tracking requests in place** {[amjad2023_blocking]} — the authors attribute the residue to requests whose initiating script their localisation step never captured, which is itself a warning about script-attribution completeness. Selective blocking of a curated tracking-script set gives a 57% reduction in tracking requests for an 11% reduction in functional ones — a better trade, but the mixed scripts are exactly the ones it cannot touch. Dropping to method granularity **reduces major breakage by 3.8× at the same level of tracking prevention** {[amjad2023_blocking]}.
+The practical consequence is measured directly on a crawl of 100K sites sampled from the Tranco top million {[amjad2023_blocking]}. Blocking **every** script cuts tracking requests from 1,175,033 to 265,101 — 22.6% remain — but leaves 75.9% of functional requests standing and **breaks legitimate functionality on roughly two-thirds (66%) of tested webpages**.((The paper's prose sentence reports this pair the other way round — //"22% of functional requests and 76% of tracking requests remain after blocking all JS scripts"// — which contradicts its own Table 2 (tracking 1,175,033 → 265,101; functional 4,279,844 → 3,248,767) and its parallel sentence for the selective configuration. The figures quoted here are read from Table 2. Checked against the published PDF on 2026-08-06.)) Blocking only a curated set of tracking scripts is the better trade: 95% of functional requests persist while 30% of tracking requests survive, or per webpage a 57% reduction in tracking requests for an 11% reduction in functional ones. But the mixed scripts are exactly the ones it cannot touch, and dropping to method granularity **reduces major breakage by 3.8× at the same level of tracking prevention**.
 NoT.js {[amjad2024_notjs]} is the current state of the art at that granularity. It builds a graph from the **dynamic execution context** of each function call — the call stack and calling context, collected by its own Chrome extension over the DevTools Protocol's ''Runtime.CallFrame'' and ''Debugger.Scope'' — trains a random forest on it, and then generates surrogate scripts that neutralise the tracking functions while leaving the rest of the file intact. Surrogate generation is not new — SugarCoat {[smith2021_sugarcoat]} already patched scripts automatically, producing replacements for 231 unique in-the-wild scripts, and ships some of them in Brave — but its input set is whatever a filter-list author has already written an //exception// rule for. NoT.js picks the functions itself. It reports 94% precision and 98% recall on function-level tracking detection over the top 10K, classifying 32.1% of the 2,088K JavaScript functions in its dataset as tracking, and 0% major / 8% minor breakage in a 50-site manual study against WebGraph's 6% / 10%.((Verified against the paper's own text and the arXiv preprint [[https://arxiv.org/abs/2405.18385|2405.18385]] on 2026-08-06. Artifact: [[https://github.com/hadiamjad/Not.js|github.com/hadiamjad/Not.js]] — MIT, **no commits since 2024-07-18**. Treat it as a frozen research artifact, not a maintained tool.))
@@ Line 57: / Line 57: @@
 ===== What This Literature Actually Is =====
-Everything in this section comes from a structured extraction over **4,322 full-text papers** from CCS, IMC, NDSS, PETS, USENIX Security, TheWebConf and IEEE S&P, 2010–2024, one record per paper with a verbatim evidence quote per claim. The population here is **160 papers that analyse or classify JavaScript running in a browser** — how that population was built, and what it misses, is at [[#Reproducing These Figures]].
+Everything in this section comes from a structured extraction over **5,859 full-text papers** from CCS, IMC, NDSS, PETS, USENIX Security, TheWebConf and IEEE S&P, 2010–2026, one record per paper with a verbatim evidence quote per claim. The 2025 and 2026 venue-years are provisional — CCS and IMC 2026 have not been held and two more 2026 venue-years are incompletely selected. The population here is **206 papers that analyse or classify JavaScript running in a browser** — how that population was built, and what it misses, is at [[#Reproducing These Figures]].
 ==== Neither obvious search handle finds it ====
@@ Line 64: / Line 64: @@
 ^ Handle ^ Papers ^ Of which measure the web ^ Share ^
-| Used or produced a ''program-analysis'' tool | 959 | 215 | 22.4% |
+| Used or produced a ''program-analysis'' tool | 1,385 | 299 | 21.6% |
-| ''studyTypes'' includes ''code-or-binary-analysis'' | 1,063 | 249 | 23.4% |
+| ''studyTypes'' includes ''code-or-binary-analysis'' | 1,484 | 347 | 23.4% |
-And the program-analysis tools these venues actually use are not JavaScript tools: LLVM (66 papers), Soot (61), IDA Pro (36), FlowDroid (34), angr (28), Ghidra (24), Androguard (23), Z3 (22), Apktool (21). **Esprima, at 15 papers, is the highest-ranked JavaScript parser in the whole corpus.** In a broad security corpus, "program analysis" means binaries, Android apps and smart contracts; web-script analysis is a small minority inside it, and shares almost no toolchain with the rest.
+And the program-analysis tools these venues actually use are not JavaScript tools: LLVM (101 papers), Soot (81), IDA Pro (62), FlowDroid (48), angr (39), Z3 (38), Ghidra (38), CodeQL (34), Androguard (33), Apktool (30). **Esprima, at 23 papers, is the highest-ranked JavaScript parser in the whole corpus.** In a broad security corpus, "program analysis" means binaries, Android apps and smart contracts; web-script analysis is a small minority inside it, and shares almost no toolchain with the rest.
-==== Where the 160 papers are ====
+==== Where the 206 papers are ====
 ^ Venue ^ Corpus papers ^ JS-analysis papers ^ Share of venue ^
-| USENIX Security | 1,117 | 39 | 3.5% |
+| USENIX Security | 1,410 | 44 | 3.1% |
-| TheWebConf | 713 | 34 | 4.8% |
+| TheWebConf | 843 | 41 | 4.9% |
-| CCS | 889 | 29 | 3.3% |
+| CCS | 990 | 33 | 3.3% |
-| IMC | 559 | 20 | 3.6% |
+| IMC | 638 | 24 | 3.8% |
-| PETS | 355 | 18 | 5.1% |
+| IEEE S&P | 767 | 22 | 2.9% |
-| NDSS | 419 | 15 | 3.6% |
+| NDSS | 701 | 21 | 3.0% |
-| IEEE S&P | 270 | 5 | 1.9% |
+| PETS | 510 | 21 | 4.1% |
 ^ Period ^ Corpus papers ^ JS-analysis papers ^ Per 1,000 corpus papers ^
-| 2010–2013 | 468 | 11 | 23.5 |
+| 2010–2013 | 511 | 14 | 27.4 |
-| 2014–2017 | 711 | 32 | 45.0 |
+| 2014–2017 | 769 | 34 | 44.2 |
-| 2018–2021 | 1,403 | 58 | 41.3 |
+| 2018–2021 | 1,439 | 61 | 42.4 |
-| 2022–2024 | 1,740 | 59 | 33.9 |
+| 2022–2024 | 1,955 | 68 | 34.8 |
+| 2025–2026 //(provisional)// | 1,185 | 29 | 24.5 |
-The field grew sharply into the mid-2010s and has been flat-to-declining as a share of these venues since. That is not a decline in importance — it is the topic being absorbed into tracking, fingerprinting and supply-chain papers that no longer describe themselves as JavaScript analysis.
+The field grew sharply into the mid-2010s and has been declining as a share of these venues since. That is not a decline in importance — it is the topic being absorbed into tracking, fingerprinting and supply-chain papers that no longer describe themselves as JavaScript analysis.
 ==== What they are about ====
@@ Line 92: / Line 93: @@
 Ranked, not measured: ''detection.phenomenon'' is one of the least reproducible fields in the extraction (~20% exact-string agreement between independent runs), so this is a ranking with a printed residue, never a set of percentages.
-^ Research family ^ Papers ^ Share of 160 ^
+^ Research family ^ Papers ^ Share of 206 ^
-| Tracking- and advertising-script classification | 25 | 15.6% |
+| Tracking- and advertising-script classification | 29 | 14.1% |
-| Malicious-script and cloaking detection | 21 | 13.1% |
+| Fingerprinting-script detection | 24 | 11.7% |
-| Third-party libraries, inclusion and supply chain((The oldest strand of this family and still the most-cited entry point: Lauinger et al. {[lauinger2017_thou]} found **87.7% of Alexa top-75K sites and 46.5% of .com sites** using at least one of 72 catalogued JavaScript libraries, and **37.8% of Alexa sites running at least one version with a known vulnerability** (9.7% two or more).)) | 20 | 12.5% |
+| Malicious-script and cloaking detection | 24 | 11.7% |
-| Fingerprinting-script detection | 19 | 11.9% |
+| Third-party libraries, inclusion and supply chain((The oldest strand of this family and still the most-cited entry point: Lauinger et al. {[lauinger2017_thou]} found **87.7% of Alexa top-75K sites and 46.5% of .com sites** using at least one of 72 catalogued JavaScript libraries, and **37.8% of Alexa sites running at least one version with a known vulnerability** (9.7% two or more).)) | 24 | 11.7% |
-| Web-API usage measurement | 16 | 10.0% |
+| Web-API usage measurement | 22 | 10.7% |
-| Client-side vulnerabilities (XSS, CSP, taint flows) | 12 | 7.5% |
+| Client-side vulnerabilities (XSS, CSP, taint flows) | 18 | 8.7% |
-| Browser-extension scripts | 7 | 4.4% |
+| Browser-extension scripts | 8 | 3.9% |
-| Data leakage by scripts | 6 | 3.8% |
+| Data leakage by scripts | 8 | 3.9% |
-| Script change and identity over time | 6 | 3.8% |
+| Script performance, size and dead code | 8 | 3.9% |
-| Cryptojacking | 5 | 3.1% |
+| Script change and identity over time | 7 | 3.4% |
-| WebAssembly (non-mining) | 4 | 2.5% |
+| Cryptojacking | 5 | 2.4% |
-| Script performance, size and dead code | 3 | 1.9% |
+| WebAssembly (non-mining) | 5 | 2.4% |
- detection tuples did not fold into any family and are printed by the report script rather than dropped.
+ detection tuples did not fold into any family and are printed by the report script rather than dropped — a slightly larger share of a larger population than the 45 of the earlier corpus, so this fold has aged well where others have not.
-The shape to notice: **the privacy reader's own family is the largest but is barely a quarter of the field.** If you search these venues for "JavaScript detection" you will mostly get client-side vulnerability and malware papers, which use the same parsers and the same instrumented browsers on a different question. They are worth reading for method and misleading as related work.
+The shape to notice: **the privacy reader's own two families — tracking/advertising classification and fingerprinting-script detection — lead the ranking, and together they are barely a quarter of the field (53 of 206).** If you search these venues for "JavaScript detection" you will mostly get client-side vulnerability and malware papers, which use the same parsers and the same instrumented browsers on a different question. They are worth reading for method and misleading as related work.
 ==== Anchor papers to read first ====
@@ Line 119: / Line 120: @@
 <WRAP important>
-This corpus ends in 2024. A ranking of what the 2010–2024 literature //did// is a fact about the literature, not advice about what to do now. The table below dates each method and states its status as of **2026-08-06**; the rows marked //current// were checked against post-2024 work outside the corpus.
+This corpus now reaches 2026, but its 2025 and 2026 venue-years are provisional and thin. A ranking of what the 2010–2026 literature //did// is a fact about the literature, not advice about what to do now. The table below dates each method and states its status as of **2026-08-06**; the rows marked //current// were checked against post-2024 work outside the corpus, and the tool table further down now gives partial corpus support for two of them.
 </WRAP>
@@ Line 148: / Line 149: @@
 **LLM-based classification of web scripts is, as of 2026-08-06, essentially absent from the peer-reviewed literature.** A targeted search across PETS 2025/2026, USENIX Security 2025, NDSS 2025/2026, IMC 2025, TheWebConf 2025/2026, CCS 2025 and arXiv found no paper that classifies web scripts as trackers with a language model, or that uses one to summarise a script's privacy-relevant behaviour. The nearest work is adjacent rather than on-point: LLM-aided **deobfuscation** feeding a graph classifier for JavaScript //malware//,((//Breaking Obfuscation: Cluster-Aware Graph with LLM-Aided Recovery for Malicious JavaScript Detection//, [[https://arxiv.org/abs/2507.22447|arXiv:2507.22447]], 2025.)) LLM screening of malicious npm packages, and ''humanify'', which uses a model only to //suggest identifier names// during de-minification.(([[https://github.com/jehna/humanify|github.com/jehna/humanify]], v3.1.1, checked 2026-08-06. The AST rewrite is done by ''oxc''; the model only proposes names.))
-<wrap todo>Treat this as an opportunity, not a settled answer. If you are planning an LLM-based script classifier, you are not late — but you also have no baseline to cite, so budget for building one, and for the reviewer question about cost, reproducibility and prompt/version drift that this page cannot yet answer for you.</wrap>
+<WRAP todo>Treat this as an opportunity, not a settled answer. If you are planning an LLM-based script classifier, you are not late — but you also have no baseline to cite, so budget for building one, and for the reviewer question about cost, reproducibility and prompt/version drift that this page cannot yet answer for you.</WRAP>
 ==== Two 2025 results that change how you design a crawl ====
@@ Line 157: / Line 158: @@
 ===== Ground Truth, Which Is This Field's Weakest Link =====
-Of the 160 papers, **153 record at least one classification task**. What they classify //with//:
+Of the 206 papers, **198 record at least one classification task**. What they classify //with//:
-^ ''classification.method'' ^ Papers ^ Share of 153 ^
+^ ''classification.method'' ^ Papers ^ Share of 198 ^
-| heuristic-rules | 80 | 52.3% |
+| heuristic-rules | 108 | 54.5% |
-| manual-labelling | 54 | 35.3% |
+| manual-labelling | 70 | 35.4% |
-| third-party-service | 47 | 30.7% |
+| third-party-service | 53 | 26.8% |
-| blocklist | 38 | 24.8% |
+| blocklist | 52 | 26.3% |
-| curated-database | 31 | 20.3% |
+| curated-database | 40 | 20.2% |
-| supervised-ml | 26 | 17.0% |
+| dynamic-analysis | 33 | 16.7% |
-| regex-or-signature | 24 | 15.7% |
+| supervised-ml | 31 | 15.7% |
-| dynamic-analysis | 21 | 13.7% |
+| regex-or-signature | 27 | 13.6% |
-| static-analysis | 16 | 10.5% |
+| static-analysis | 25 | 12.6% |
-| unsupervised-ml | 11 | 7.2% |
+| graph-analysis | 13 | 6.6% |
-| graph-analysis | 9 | 5.9% |
+| other | 11 | 5.6% |
-| other | 9 | 5.9% |
+| unsupervised-ml | 11 | 5.6% |
+| **llm** | **2** | **1.0%** |
-And what they treat as truth. These 153 papers produce **278 distinct free-text ground-truth strings**, folded here into families; 52 tuples did not fold and are printed by the report script.
+The ''llm'' row is new: on the 4,322-paper corpus this enum never fired for a JavaScript-classification task at all. Two papers is not a trend, and it does not contradict the finding below that no peer-reviewed paper yet classifies web scripts //as trackers// with a language model.
-^ Ground-truth family ^ Papers ^ Share of 153 ^
+And what they treat as truth. These 198 papers produce **351 distinct free-text ground-truth strings**, folded here into families; 80 tuples did not fold and are printed by the report script.
-| **Authors' own manual inspection** | 84 | **54.9%** |
-| Filter list or tracker database | 36 | 23.5% |
+^ Ground-truth family ^ Papers ^ Share of 198 ^
-| Malware / phishing blacklist service | 11 | 7.2% |
+| **Authors' own manual inspection** | 102 | **51.5%** |
-| Vulnerability database | 7 | 4.6% |
+| Filter list or tracker database | 44 | 22.2% |
-| Synthetic or seeded ground truth | 5 | 3.3% |
+| Malware / phishing blacklist service | 13 | 6.6% |
-| Prior published dataset or labels | 4 | 2.6% |
+| Synthetic or seeded ground truth | 9 | 4.5% |
-| Spec or documentation | 3 | 2.0% |
+| Vulnerability database | 9 | 4.5% |
-| Library signature catalogue | 1 | 0.7% |
+| Spec or documentation | 5 | 2.5% |
-| Recruited or external annotators | 1 | 0.7% |
+| Prior published dataset or labels | 4 | 2.0% |
+| Library signature catalogue | 1 | 0.5% |
+| Recruited or external annotators | 1 | 0.5% |
 <WRAP important>
-**The most common ground truth for "is this script a tracker" is the authors reading the script.** That is a defensible choice at small scale and it is what most of this field does — but it means the labels are unpublished, unaudited and, in most cases, produced by the same people who built the classifier. Only 36 of 153 papers anchor to a filter list or tracker database, and exactly one used annotators from outside the author team.
+**The most common ground truth for "is this script a tracker" is the authors reading the script.** That is a defensible choice at small scale and it is what most of this field does — but it means the labels are unpublished, unaudited and, in most cases, produced by the same people who built the classifier. Only 44 of 198 papers anchor to a filter list or tracker database, and exactly one used annotators from outside the author team.
 </WRAP>
-Validation, counted per paper rather than per tuple: **115 of 153 (75.2%) report some validation** for at least one classification, and **38 (24.8%) report none anywhere**. Where validation exists it is overwhelmingly more manual inspection (102 papers, 66.7%); comparison to another method 22 (14.4%), cross-validation 14 (9.2%), a held-out test set 9 (5.9%).
+Validation, counted per paper rather than per tuple: **146 of 198 (73.7%) report some validation** for at least one classification, and **52 (26.3%) report none anywhere**. Where validation exists it is overwhelmingly more manual inspection (129 papers, 65.2%); comparison to another method 33 (16.7%), cross-validation 16 (8.1%), a held-out test set 12 (6.1%).
 ==== If you use a filter list as ground truth ====
@@ Line 222: / Line 226: @@
 | Static AST / bytecode | what the code //could// do, on code you never executed | what it actually did, and anything behind ''eval'' |
-Which of these the 160 papers actually name:
+Which of these the 206 papers actually name:
+^ Tool family ^ Papers ^ Share of 206 ^
+| Esprima | 21 | 10.2% |
+| OpenWPM | 19 | 9.2% |
+| PageGraph | 8 | 3.9% |
+| Project Foxhound (taint tracking) | 8 | 3.9% |
+| VisibleV8 | 8 | 3.9% |
+| js-beautify | 7 | 3.4% |
+| V8 (as an analysis substrate) | 7 | 3.4% |
+| Closure Compiler | 5 | 2.4% |
+| Babel, FP-Inspector, Jalangi, WABT | 4 each | 1.9% |
+| JStap | 3 | 1.5% |
+| jsdom | 2 | 1.0% |
+| Acorn, AdGraph, Emscripten, JSgraph, JSNice, Khaleesi, Rhino, SpiderMonkey, TAJS, UglifyJS, unnamed deobfuscator | 1 each | 0.5% |
-^ Tool family ^ Papers ^ Share of 160 ^
+Two rows moved enough to matter. **Esprima has overtaken OpenWPM** as the most-named tool, and **Project Foxhound went from 2 papers to 8 and PageGraph from 4 to 8** — the taint-tracking and page-graph instruments the //Methods// table above calls current are the ones the 2025–2026 papers actually picked up. That is the rare case where the corpus confirms a currency judgement instead of only dating it.
-| OpenWPM | 17 | 10.6% |
-| Esprima | 16 | 10.0% |
-| VisibleV8 | 8 | 5.0% |
-| js-beautify | 7 | 4.4% |
-| V8 (as an analysis substrate) | 5 | 3.1% |
-| Closure Compiler | 4 | 2.5% |
-| FP-Inspector | 4 | 2.5% |
-| PageGraph | 4 | 2.5% |
-| WABT | 3 | 1.9% |
-| Babel, jsdom, JStap, Project Foxhound | 2 each | 1.3% |
-| Acorn, AdGraph, Emscripten, Jalangi, JSgraph, JSNice, Khaleesi, Rhino, SpiderMonkey, TAJS, UglifyJS, unnamed deobfuscator | 1 each | 0.6% |
-**91 of 160 papers (56.9%) name no JavaScript-analysis tool at all** — they wrote their own parser, regexes or instrumentation and did not name it. That is the field's reproducibility problem in one number.
+**120 of 206 papers (58.3%) name no JavaScript-analysis tool at all** — they wrote their own parser, regexes or instrumentation and did not name it. That is the field's reproducibility problem in one number.
 ==== Maintenance status, because half of these are frozen ====
@@ Line 262: / Line 269: @@
   - **Minification** is near-universal and destroys the identifier names your features probably depend on. Skolka et al. {[skolka2019_anything]} found **38.5% of all crawled scripts** had gone through some transformation.
-  - **Obfuscation proper is rare but definitionally contested.** The same study puts deliberate obfuscation at **0.67% of scripts (2,842)**; Sarker et al. {[sarker2020_hiding]} — defining it as invoking APIs that static analysis cannot resolve — put it at **95.90% of the 77,423 Alexa top-100K domains for which they obtained script data** (about 74% of the top 100K) loading at least one obfuscated script, with 5,028 distinct obfuscated scripts calling ''eval''. Both are correct. Report your definition and your unit or the number means nothing.
+  - **Obfuscation proper is rare but definitionally contested.** The same study puts deliberate obfuscation at **0.67% of scripts (2,842)**; Sarker et al. {[sarker2020_hiding]} — defining it as invoking APIs that static analysis cannot resolve — put it at **95.90% of domains loading at least one obfuscated script** — where the denominator is the 77,423 Alexa top-100K domains for which they obtained script data at all, so ~74% of the top 100K rather than 95.9% of it, with 5,028 distinct obfuscated scripts calling ''eval''. Both are correct. Report your definition and your unit or the number means nothing.
   - **Bundling** merges unrelated code into one artifact — 40% of sites {[rack2023_jackinthebox]}.
   - **Dynamic code** defeats it outright. Forced-execution engines exist precisely for this: J-Force, and more recently FV8 {[pantelaios2024_fv8]}, which selectively forces execution of the APIs that conditionally inject dynamic code (''eval'', ''setTimeout'' and friends) to reveal evasive behaviour and surfaced 28 evasion categories across npm packages and browser extensions.
@@ Line 270: / Line 277: @@
 ===== Crawl Methodology, Against the Corpus =====
-Of the 160 papers, 128 (80.0%) ran a crawl and 126 recorded a configuration. They report better than the corpus average on every axis — and still leave the two axes that matter most for this topic largely unstated.
+Of the 206 papers, 165 (80.1%) ran a crawl and 163 recorded a configuration. They report better than the corpus average on every axis — and still leave the two axes that matter most for this topic largely unstated.
-^ ''crawlConfig'' field ^ States a value ^ Share of 126 ^ All 829 crawling papers ^
+^ ''crawlConfig'' field ^ States a value ^ Share of 163 ^ All 1,080 papers with a crawl configuration ^
-| Statefulness | 36 | 28.6% | 19.9% |
+| Statefulness | 45 | 27.6% | 20.3% |
-| Interaction depth | 112 | 88.9% | 78.6% |
+| Interaction depth | 147 | 90.2% | 77.9% |
-| Consent action | 65 | 51.6% | 32.6% |
+| Consent action | 79 | 48.5% | 32.3% |
-| Headless or headful | 28 | 22.2% | 13.4% |
+| Headless or headful | 31 | 19.0% | 13.0% |
-| Authentication | 104 | 82.5% | 71.5% |
+| Authentication | 134 | 82.2% | 72.1% |
-| Browser named | 101 | 80.2% | 47.6% |
+| Browser named | 132 | 81.0% | 49.0% |
 <WRAP important>
-**22.2% state headless-or-headful, and for this topic that is not a formality.** Two measurements in the corpus quantify the cost:
+**19.0% state headless-or-headful, and for this topic that is not a formality.** Two measurements in the corpus quantify the cost:
   * Jueckstock et al. {[jueckstock2021_realistic]} found about **10% of script families show consistent browser-configuration bias**, and traced one concretely: Crazyegg's script invoked **fewer than 15 browser APIs under a naive crawl but nearly 60 under a stealth crawl**, because a function named ''uaBot'' short-circuits on bot detection. A naive crawl does not under-count that script — it observes a **different program**.
@@ Line 291: / Line 298: @@
 **The consent row deserves its own sentence.** On EU-facing sites a large part of the advertising and analytics stack is loaded by the consent management platform //after// a consent click, so a crawl that never interacts with the banner measures a different script population from one that accepts — and a third one from one that rejects. Half these papers do not say which they did. Decide deliberately, state it, and see [[Privacy:Consent]] for how to drive the interaction; the same choice is what makes [[Privacy:Cookies]] figures comparable or not.
-Artifact release is a bright spot: **46 of the 59 papers from 2022–2024 (78.0%) released an artifact link, against 64.7% for the corpus over the same years.** Only 13 of 160 (8.1%) assess a law — GDPR in 11 — against 6.1% corpus-wide, and 19 (11.9%) recruited participants.
+Artifact release is a bright spot: **53 of the 68 papers from 2022–2024 (77.9%) released an artifact link, against 65.0% for the corpus over the same years**, and 27 of 29 (93.1%) in the provisional 2025–2026 window against 76.5%. Only 19 of 206 (9.2%) assess a law — GDPR in 17 — against 6.9% corpus-wide (402 of 5,859), and 24 (11.7%) recruited participants.
 ===== What to Report =====
@@ Line 303: / Line 310: @@
   - **The crawl.** Browser and version, headless or headful, stateful or stateless, consent action, interaction depth, vantage point, and — because of the results above — whether you checked for divergent behaviour under a stealth or headful configuration.
   - **Handling of what you could not analyse.** Inline scripts, ''eval''-generated code, workers, WebAssembly, ''blob:''/''data:'' sources, and scripts that deleted themselves. Report the count you dropped rather than letting it vanish into the denominator.
-  - **Reproducibility.** The script corpus if licensing allows, the feature extractor, and the trained model. This subfield is good at this — 78% since 2022 — so a paper without it stands out.
+  - **Reproducibility.** The script corpus if licensing allows, the feature extractor, and the trained model. This subfield is good at this — 78% for 2022–2024 and higher since — so a paper without it stands out.
 ===== Reproducing These Figures =====
@@ Line 315: / Line 322: @@
 Three families are in the tool table but deliberately excluded from the //membership// rule, because their non-analysis use is large and it was measured rather than assumed: OpenWPM alone dragged in an IPv6-scanning study, a QUIC website-fingerprinting paper and an HSTS study that used it as a plain crawler; Emscripten dragged in two papers that //compiled to// WebAssembly; SpiderMonkey dragged in RIDL, where the engine is the victim of a CPU attack. Three further papers — Spectre, Fallout and RIDL — were removed by hand after reading, with the reason recorded inline in the script: JavaScript is their exploit vector, not their object of study.
-^ Signal combination ^ Papers ^ Share of 160 ^
+^ Signal combination ^ Papers ^ Share of 206 ^
-| classification only | 43 | 26.9% |
+| classification only | 57 | 27.7% |
-| detection only | 42 | 26.3% |
+| detection only | 53 | 25.7% |
-| detection + classification | 23 | 14.4% |
+| detection + classification | 30 | 14.6% |
-| tool only | 21 | 13.1% |
+| tool only | 26 | 12.6% |
-| tool + detection + classification | 12 | 7.5% |
+| tool + detection + classification | 19 | 9.2% |
-| tool + detection | 11 | 6.9% |
+| tool + detection | 13 | 6.3% |
-| tool + classification | 8 | 5.0% |
+| tool + classification | 8 | 3.9% |
-The whole rule, including every fold and the reason for each hand exclusion, is below. It needs only ''extractions.jsonl'' and the shared ''lib.mjs'' helper; the companion ''report_javascript.mjs'' prints every figure on this page with its denominator, the residue of each fold, and the full 160-paper list.
+The whole rule, including every fold and the reason for each hand exclusion, is below. It needs only ''extractions.jsonl'' and the shared ''lib.mjs'' helper; the companion ''report_javascript.mjs'' prints every figure on this page with its denominator, the residue of each fold, and the full 206-paper list. Every query behind this section is on [[provenance:privacy:javascript]]; corpus-level caveats are on [[literature:corpus]].
 <file javascript js_fold.mjs>
@@ Line 332: / Line 339: @@
 // JavaScript a page runs". Neither of the obvious schema handles answers it:
 //
-//   * `tools[].category == "program-analysis"` fires on 959 papers (used or
+//   * `tools[].category == "program-analysis"` fires on 1,385 papers (used or
 //     produced), but that category is dominated by binary, Android and
-//     smart-contract analysis (LLVM 66, Soot 61, IDA Pro 36, FlowDroid 34,
+//     smart-contract analysis (LLVM 101, Soot 81, IDA Pro 62, FlowDroid 48,
-//     angr 28). Esprima, the highest-ranked JavaScript parser, is 15.
+//     angr 39). Esprima, the highest-ranked JavaScript parser, is 23.
-//   * `studyTypes` includes `code-or-binary-analysis` on 1,063 papers, same
+//   * `studyTypes` includes `code-or-binary-analysis` on 1,484 papers, same
 //     problem, and it is the least reproducible field in the schema (57%).
 //
@@ Line 566: / Line 573: @@
 // ---------------------------------------------------------------------------
-// Ground-truth sources for a script-classification task. 278 distinct strings
+// Ground-truth sources for a script-classification task. 351 distinct strings
 // across the population, so again a RANKING. Ordered; first match wins, with
 // the named external resources ahead of the generic "manual" phrasings,
@@ Line 592: / Line 599: @@
 ==== Methodology and limitations of these figures ====
-  * **Seven venues only.** CCS, IMC, NDSS, PETS, USENIX Security, TheWebConf, IEEE S&P, 2010–2024. IEEE S&P is only 43% retrieved, and **EuroS&P, ACSAC, RAID, AsiaCCS, CHI and SOUPS are absent entirely** — for this topic ACSAC and EuroS&P are a real hole, since a good deal of web-script security work lands there. Every claim here is a claim about those seven venues.
+  * **Seven venues only**, 2010–2026, with 2025 and 2026 provisional. Which venues, which years, what each stage of the selection funnel costs and which venue-years are empty are on [[literature:corpus]] and are not restated here. For //this// topic ACSAC and EuroS&P are a real hole, since a good deal of web-script security work lands there. Every claim here is a claim about those seven venues.
-  * **160 is a floor, and it has a false-positive tail.** Papers whose extraction never names a script as the object of detection are missing; conversely a handful of papers in the population (an e-voting client audit, a router-attack paper, a PHP injection-sink study) analyse JavaScript incidentally. The report script prints the full list so you can judge.
+  * **206 is a floor, and it has a false-positive tail.** Papers whose extraction never names a script as the object of detection are missing; conversely a handful of papers in the population (an e-voting client audit, a router-attack paper, a PHP injection-sink study) analyse JavaScript incidentally. The report script prints the full list so you can judge.
   * **Not every field can carry a percentage.** ''crawlConfig.*'', ''legal.law'' and ''platforms'' reproduce to within a few points on a repeat extraction and carry the figures here. ''classification.method'' agrees on only **58%** of papers between two runs of the same schema over the same text, so its table above is a **rough share, not a precise figure** — a repeat extraction moves those rows. ''detection.phenomenon'', ''classification.resourceName'' and ''groundTruthSource'' agree on roughly 20% of exact strings, which is what the folding is for and why the family and ground-truth tables print their residue.
   * **Silence is not absence.** "Does not state whether it ran headless" means the paper did not say. These are reporting figures, not practice figures.
-  * **Every quoted figure was checked against the paper's own text.** The prevalence values in the extraction are model summaries, so each number reproduced on this page was re-located in ''paper.cols.txt'' after whitespace normalisation. 0.9% of quotes in the dataset cannot be located in their source at all.
+  * **Every quoted figure was checked against the paper's own text.** The prevalence values in the extraction are model summaries, so each number reproduced on this page was re-located in ''paper.cols.txt'' after whitespace normalisation. The dataset's own "0.9% of quotes cannot be located" figure was measured on the earlier 4,322-paper run and has not been re-measured.
+  * **Every query behind this section, the report script and its unedited output** are on [[provenance:privacy:javascript]]; corpus-level caveats are on [[literature:corpus]].
 ===== Open Questions =====
-  * <wrap todo>**No public, hand-labelled corpus of tracking scripts exists.** Every current method builds its own labels from filter lists plus manual inspection, which is why cross-paper comparison is impossible. A shared benchmark would do for this field what EasyList did for request blocking.</wrap>
+<WRAP todo>
-  * <wrap todo>**LLM-based script classification is unmeasured.** No peer-reviewed paper found as of 2026-08-06. The obvious study — LLM against WebGraph, AdFlush and NoT.js on a fixed script corpus, reporting cost and version drift as well as F1 — has no baseline yet.</wrap>
+  * **No public, hand-labelled corpus of tracking scripts exists.** Every current method builds its own labels from filter lists plus manual inspection, which is why cross-paper comparison is impossible. A shared benchmark would do for this field what EasyList did for request blocking.
-  * <wrap todo>**Nobody has measured how much a headless or containerised crawler under-counts //script// classification specifically.** {[jueckstock2021_realistic]} and {[annamalai2024_fpfed]} show the gap exists for API traces and fingerprinting scripts; its size for tracking-script prevalence at scale is unknown.</wrap>
+  * **LLM-based script classification is unmeasured.** No peer-reviewed paper found as of 2026-08-06. The obvious study — LLM against WebGraph, AdFlush and NoT.js on a fixed script corpus, reporting cost and version drift as well as F1 — has no baseline yet.
-  * <wrap todo>**Function-granularity blocking has no successor paper.** NoT.js {[amjad2024_notjs]} and ByteDefender {[bahrami2025_bytedefender]} both stop at detection plus surrogate generation; nobody has measured what happens when either is deployed to real users at scale, or whether trackers adapt.</wrap>
+  * **Nobody has measured how much a headless or containerised crawler under-counts //script// classification specifically.** {[jueckstock2021_realistic]} and {[annamalai2024_fpfed]} show the gap exists for API traces and fingerprinting scripts; its size for tracking-script prevalence at scale is unknown.
-  * <wrap todo>**Cross-platform divergence is a confound in every older result.** If 20.6% of scripts execute differently by platform {[zafar2025_samescript]}, every desktop-only prevalence figure in this page's tables is a measurement of the desktop path only. Re-running any of them on mobile is a well-defined study.</wrap>
+  * **Function-granularity blocking has no successor paper.** NoT.js {[amjad2024_notjs]} and ByteDefender {[bahrami2025_bytedefender]} both stop at detection plus surrogate generation; nobody has measured what happens when either is deployed to real users at scale, or whether trackers adapt.
+  * **Cross-platform divergence is a confound in every older result.** If 20.6% of scripts execute differently by platform {[zafar2025_samescript]}, every desktop-only prevalence figure in this page's tables is a measurement of the desktop path only. Re-running any of them on mobile is a well-defined study.
+</WRAP>
 ===== Related Pages =====
@@ Line 610: / Line 620: @@
   * [[Privacy:Requests]] — classifying at the request/URL layer, where filter lists live, and why they cap this page's ground truth.
   * [[Privacy:Cookies]] — what the scripts write; the provenance argument (a cookie set by a blocked resource) is the same idea one layer down.
-  * [[Privacy:Fingerprinting]] — 42.4% of browser-fingerprinting papers are really detecting //scripts//, so that page and this one share a method.
+  * [[Privacy:Fingerprinting]] — 39.8% of browser-fingerprinting papers are really detecting //scripts//, so that page and this one share a method.
-  * [[Programming:Crawler]] — the instrumentation this page assumes you already have, compared in detail. Its per-tool pages ([[Programming:Crawler:OpenWPM]], [[Programming:Crawler:PageGraph]], [[Programming:Crawler:Foxhound]]) are promised but not yet written.
+  * [[Programming:Crawler]] — the instrumentation this page assumes you already have, compared in detail. Its per-tool pages [[Programming:Crawler:OpenWPM]] and [[Programming:Crawler:PageGraph]] are written; [[Programming:Crawler:Foxhound]] is still promised.
-  * [[Programming:Stateful stateless]] — only 28.6% of these papers state it, and a stateless crawl sees first-visit script behaviour only.
+  * [[Programming:Stateful stateless]] — only 27.6% of these papers state it, and a stateless crawl sees first-visit script behaviour only.
   * [[Design:Website classification]] — where script classification sits in the wider taxonomy.
   * [[Design:Crawling location]] — the vantage-point half of the "the site served you different code" problem.