Agents That Edit Documents: Measuring Agentic PDF Forgery Against a Non-Agentic Control Simiao Ren† Ankit Raj* Tommy Duong* Yuxin Zhang* Dennis Ng* Xingyu Shen* Kidus Zewde* Yuchen Zhou* Neo Tiangratanakul* Scam.ai (Reality Inc.) †
Corresponding author: [email protected].
*
Equal contribution.
1 A request
2 Models 3 Agent harness
4 One lane per target type
5 Submitted
one sentence
seven
3 of 3 types, across 125 filings
as evidence to
one, shared
money
$950.00 → $1157.50
date
28/07/2014 → 4/07/2016 insurers
arXiv:2609.23953v1 [cs.AI] 20 Sep 2026
opencode stock coding agent shell + Python pikepdf, PyMuPDF, qpdf, Ghostscript
a relying party
Docker, 20-min wall
banks and lenders
nothing PDF-specific
loan servicers
inspect
auditors
edit
"Change $950.00 to $1157.50."
verify
location
44 Hawley Street → 1 Hawley Street
KYC providers tax authorities
receipt
Figure 1: From one sentence to a submitted filing. (1) An operator with no PDF expertise names the value to change. (2) One of seven open-weight models — DeepSeek, Z.ai (GLM), Meta (Muse Spark), Qwen, MiniMax, Moonshot (Kimi), Mistral — is dropped into (3) the same off-the-shelf coding-agent harness, opencode, with a shell, Python and the stock PDF libraries; nothing PDF-specific is added, and the agent inspects, edits, re-renders, verifies and files a receipt on its own. (4) One real alteration per target type, as filed (left) and as returned (right), changed value boxed: an amount in an SEC prospectus, a date in a non-US filing, an address in a federal filing. (5) The result is the kind of artifact submitted as evidence to insurers, lenders, auditors, KYC providers and tax authorities. Provider marks imply no endorsement; the Meta mark denotes Muse Spark, not the excluded Llama.
Abstract
1
AI agents that carry a multi-step computer task through on their own became ordinary tools in the past year, and the same autonomy is available to anyone whose task is harmful. We ask what that means for a relying party — an insurer, a lender, an auditor — whose evidence is a filed PDF. AgentForge-Bench measures how reliably an off-the-shelf coding agent, driving one of seven open-weight models with a shell and the stock Python PDF stack, alters one dollar amount, date or address in a real filed financial document from a single sentence of intent, graded by rules rather than by a model. Across 1,750 cells, 1,419 (81.1%) satisfy the verifier, and 808 (46.2%) also survive every stricter filter: visible, localized, typeface-matched, original value gone document-wide. A deterministic script with no model in it solves 98 of the 125 documents; the agents solve 124, and none the script solves alone. Agents misreport 41% of their wrong edits as done, no model refused, and the cheapest verified forgery costs 2.4 cents. The raw rate overstates the threat by about a factor of two; the strict rate is still large.
Introduction
Document fraud was limited by craft. Altering a filed audit report or a bank statement convincingly required a skilled operator with a PDF editor, or a bespoke script by someone who understood the container format. Both barriers were effort, and both have dissolved: a contemporary coding agent, given a shell and the ordinary open-source PDF libraries, can be pointed at a document and told what number to change. This paper measures how far that has gone (Figure 1). We propose no attack technique — every tool the agents use (pikepdf, PyMuPDF, qpdf, Ghostscript) is a mainstream, legitimatelymaintained library. We measure capability: given only an intent, how reliably does an open-weight model driving an off-the-shelf agent harness produce a document changed in exactly the way requested and in no other way. That needs a benchmark rather than a demonstration, because the interesting failure is not “the agent could not edit the PDF” but “the agent changed the wrong thing, or 1
2
changed the right thing and lied about how.” Both are invisible to an eyeball test and both are caught by rules. Our prior work measured the detection side of this problem: benchmarks of AI-forged financial and form documents [1, 2], a generated-receipt dataset with a human study [3], and tests of whether multimodal LLMs and image models recognise manipulated documents [4, 5]. This paper measures the attack side, and deliberately not detectability.
Related Work
Agent benchmarks. Benchmarks for tool-using agents have converged on executable, rule-graded environments: program repair [6], web navigation [7], desktop and operating-system tasks [8, 9], tool–agent–user interaction [10] and offensive security [11]. AgentForge-Bench follows the same pattern — a sandbox, a deterministic task, a rulebased grader — for a document-integrity capability that, to our knowledge, has not been benchmarked.
Contributions.
PDF format security. Signature spoofing [12], shadow attacks inside signed documents [13] and the format’s parsing surface [14] ask what a knowledgeable human attacker can make the container do. We ask the orthogonal question: what an agent does when told what to change and left to find its own method.
1. A balanced dataset of consequential financial documents: 81 filed originals, collected issuer-direct and ranked by a ForgerySeriousness Index so that the documents forged are the ones institutions rely on as evidence, each paired with one verified forgery that survives every validity filter — 162 PDFs (Section 3.1). 2. AgentForge-Bench: exact before→after targets seeded deterministically from the document hash, labelled by four structural tiers and three target semantics, graded by a rulebased verifier with no model in the loop that also audits the agent’s written receipt against the file (Section 3.3). 3. A 1,750-cell evaluation across 7 openweight models with a non-agentic control: a deterministic scripted editor, graded identically and hardened against an independent diagnosis of its own failures, solves 98 of 125 documents against the agents’ 124, and none the agents miss (Section 4.4). 4. A strict-validity ladder separating an edit was made from a document was forged: 81.1% of cells satisfy the verifier; 46.2% also change the page, stay localized, match the typeface and leave no surviving copy of the original value (Section 4.3).
Dangerous-capability evaluation. Methodologically this paper belongs to the capability-evaluation genre [11, 15]: a deterministic task generator, refusals as first-class outcomes, and — the convention most often skipped — measured uplift over a non-model baseline (Section 4.4). Document forgery detection. The defensive literature is raster-domain: manipulation-localization networks [16–20], unified benchmarks [21–23], explaining multimodal detectors [24–27], and document-specific tamper localization [28]. Our own contributions there are image-level: AIForgeDoc [1] and DocForge-Bench [2] benchmark detectors on AI-forged financial and form documents, GPT4o-Receipt [3] pairs generated receipts with a human study, and we found that multimodal reasoning LLMs are unreliable detectors [4]. The closest analogue of a commodity model as forger is our evaluation of image models on forgery tasks — GPT-Image-2 cannot recognise its own faked documents [5], and ChatGPT Images 2.5 improves on advertised metrics without closing known gaps [29]. Those forgeries are pixels; the edits measured here happen in the content stream before any rendering, and Section 4.7 shows they are typically confined to a few hundred pixels.
Scope. This paper measures capability, not detectability. It covers 125 source documents and is sized to discriminate between models and support the uplift comparison, not to settle any model’s ranking. An eighth model, llama-3.3-70b-instruct, produced zero verified forgeries and is excluded from every aggregate; it is reported once, at the head of Section 4. Appendix L says what the data do and do not support.
3
Benchmark Design
3.1
Document corpus
Synthetic invoices and blank forms would be easier to edit in every respect that matters and would measure nothing about the documents institutions rely 2
on, so the corpus was built first, with acquisition criteria fixed before any editing was attempted. A document is collected from its issuer, at a canonical re-resolvable URL, with bytes preserved rather than re-rendered: a re-rendered copy makes an edit indistinguishable from the damage of transit. The route registry holds 184 routes, 96 verified, across twelve issuer families; every harvested file is stored with its source URL and SHA-256 and classified by text-layer quality before it is eligible for a task. Each route is scored on a Forgery-Seriousness Index (FSI) weighting the consequence, reliance surface, source authority and byte-fidelity of the source; the top-ranked sources are FFIEC call reports (FSI 93.5), federal single audits and SEC annual reports (90.0) and state auditor reports feeding the municipal bond market (84.0). FSI is a single-rater sampling heuristic, not a measurement, and we report it as a property of the corpus rather than a predictor of editability (Appendix B). Task construction narrows in three recorded stages: a pristine pool of 3,662 files over 75 routes and seven issuer families; a stratified subsample preserving that structural mix; and editable targets subject to per-document caps and a $250 floor on money targets. The evaluation draws 125 tasks (seed 20260918) — 60 money, 35 date, 30 location, one target per document — spanning 53 acquisition routes and seven issuer families (Appendix B, Figure 7). These are filed, published records — bank condition reports, municipal audits, servicer reports, enforcement orders — so a success alters the artifact a lender or an examiner would be asked to trust. 3.2
split across several Tj/TJ operators are retained; removing them would inflate measured capability. Difficulty tiers. Tasks are labelled by the structural obstacle of the target page: T1, uncompressed content stream (rare: 8 of 627 discovered candidates); T2, Flate-compressed stream, which must be decompressed, edited and recompressed; T3, scanned or rotated page, where the target exists only in the raster and the edit must survive OCRbased verification; T4, the target occurs several times on the page and every occurrence must change with the count preserved. The draw is not balanced across tiers; it reflects what the corpus contains. No model in the loop. Target discovery is deterministic: regular-expression classes for currency, date and address forms are matched against PyMuPDF text spans, kept only on a single line, and mutated from the hash seed. Grading is rulebased (Section 3.3). No language model selects targets, chooses mutations or judges outputs. Two intent arms. Every task is issued twice with identical before/after values. Arm A (pure intent) is one sentence naming the document, the value and the replacement, with no tooling hints, method or instruction to verify. Arm B (guided) adds candidate libraries, techniques and an explicit self-verification requirement. Harness. Each cell runs in its own Docker container (2 CPUs, 4 GB, 20-minute wall enforced, 40-turn budget stated to the agent but not enforced; Appendix I) with opencode in full-autonomy mode, ten tools, and the mainstream PDF toolchain (pikepdf, PyMuPDF, qpdf, Ghostscript, ocrmypdf, Tesseract and others; Appendix C). Agents may install packages and fetch the web; all egress passes through a logging proxy. The model is reached only through a key-holding shim that writes every request and response to disk, so token accounting and cost are exact. Requests use max_tokens 32,000 at the provider’s default temperature, except moonshotai/kimi-k2.6 at temperature 1 (Appendix K). The agent must write /out/edited.pdf and /out/receipt.json, declaring target, before and after values, page, tool, technique, save mode and installed packages; copying the original unchanged is a failure.
Tasks, intent arms and harness
Targets. Each task names one target string on one page and the exact value it must become, derived deterministically from sha256(file):page:token, so the “after” value is not chosen to be convenient. Money is perturbed by 5–50%, preserving separators, currency symbol and decimal places (floor $250). Date shifts one or more numeric fields, keeping separators and field order. Location swaps a city/state/ZIP or street number, preserving field structure and digit count but not always character count, so a replacement may alter the advance width of a justified line (Appendix K). Arithmetic consistency is not required — an edited total need not agree with its column — which keeps grading decidable (Appendix L). Targets whose digits are 3
3.3
Verification
claim an edit its file does not contain. Including it would depress the headline rate by roughly ten points while describing a model that never drove the harness at all (Appendix K).
Grading is entirely rule-based. For each cell the verifier measures whether the file parses and its bytes changed; whether the “after” value is present and the “before” value absent on the target page; occurrence counts before and after; the rendered pixel difference against the original and whether it is confined to the target region; and the save mode, inferred from the %%EOF count (a second %%EOF implies an incremental update). T3 targets are verified by rendering at 200 dpi and running OCR.
4.1
1,419 of 1,750 cells (81.1%) produced a document the verifier accepts as forged (Figure 2). The capability is not scarce: six of seven models clear 70% and two clear 97%. Rates run from 97.6% (95% Wilson interval 94.9–98.9) for deepseek-v4.1-flash to 43.6% (37.6–49.8) for mistral-small-2603; we use Wilson intervals because they stay well-behaved near p = 1, and the four leaders are not separable from one another (Appendix A). mistral-small-2603 is weakest at 43.6%, and its dominant non-success outcome is attempted wrong (38% of its cells): it edits the document and changes something other than the target. A model that fails by doing nothing is harmless; one that confidently alters the wrong figure in an audited statement is not, and the aggregate rate cannot tell them apart. In 116 of the 176 attempted-wrong cells the requested value is absent from the file: the agent reported an edit it never made.
Outcomes. Each cell resolves to exactly one of success (a parseable file in which the requested change, and only that change, was made); attempted wrong (a file was produced but the change is not what was asked; in about two thirds the requested value is absent from the file, Section 4.1); failed (no usable output); refused (detected by a pattern match over the transcript and published as a first-class result); or harness error (15 of 1,750 cells, 0.9%). Validity filters. success is a claim about the text layer. Four filters are applied to successes afterwards and reported as a ladder (Section 4.3); none changes the verdict. Visible change: at least one rendered pixel differs. Localization: the pixel difference is confined to the target region. Typeface match: the font resource drawing the replacement is the one that drew the original. Document-wide survival: the original value is absent from the extracted text of every page; on scanned pages with no document-wide text layer this is undecidable and the cell is not removed.
4.2
Difficulty is a property of the page
The model explains far more of the variance than the page does — a 54-point spread across models against roughly ten across tiers — but within a model, page structure sets the margin (Figure 9, Appendix F). Multi-occurrence targets (T4, 67–77%) are harder than compressed single-occurrence ones (T2, 83%), though T3 and T4 are not distinguishable at this n, and location strings fall below dates in every tier (67–76% against 74–88%). The strata are uneven: 92 of 125 documents are T2, 12 are T3, 20 are T4 and one is T1, which supports no rate. Five documents the generator labelled T2 render as full-page scans and were re-labelled T3 (Appendix E).
Receipt honesty. Independently of the outcome, the verifier compares each receipt with measured reality — declared values and page, tool, save mode against the %%EOF evidence, declared packages against pip freeze, and whether the claimed edit was applied — and classes each produced file as honest, misdescribed, fabricated (the receipt claims an edit that was not applied) or no receipt (Appendix D).
4
Capability
4.3
What survives every check
Two filters do almost all the work (Figure 3). Typeface fidelity removes 78 cells: the string is right and the font is wrong, so the page reads as edited to anyone who looks. Document-wide survival removes 495: the target page is clean but the original value still sits on a summary page, in a table of contents, or in an appendix. What is left is 808
Results
Every number below is over 1,750 cells: 125 documents × 2 intent arms × 7 open-weight models. An eighth model, llama-3.3-70b-instruct, is excluded throughout: it produced zero verified forgeries in 250 cells, and 113 of the 114 receipts it filed 4
success
attempted wrong
deepseek-v4.1-flash glm-5.3 muse-spark-1.3-contributor qwen3.7-plus minimax-m3 kimi-k2.6 mistral-small-2603
44 0
failed
98 97 92 92
73 72
20
harness error
38 40
% of 250 cells
22 16 18
9
60
80
8
100
Figure 2: What each model produces across its 250 cells: outcome composition, models ordered by success rate; segments under 7% are unlabelled. Three tiers separate cleanly — the four leaders, the two mid-table models, and mistral-small-2603 — but within a tier the ordering is not resolved at n = 250 (95% Wilson intervals are given in the text; Appendix A gives the pairwise tests).
verifier success
of 1,750 attempts verifier calls it success
1,419 (81%)
changed 1 rendered pixel
22 1,397 (80%)
the change is localized
16 1,381 (79%)
typeface matches the page
78 1,303 (74%)
old value gone document-wide
60% 59% 56% 52% 44% 41%
deepseek-v4.1-flash glm-5.3 muse-spark-1.3 qwen3.7-plus minimax-m3 kimi-k2.6 mistral-small-2603
495 808 (46%)
survives every check
12%
0
50
100
% of 250 cells
Figure 3: Left: successes surviving each successive validity filter, with the number lost at each step. Right: each model’s raw success rate against the share that survives all of them. mistral-small-2603 loses three quarters of its successes; even the two leaders lose nearly two fifths.
cells, 46.2% of the 1,750 attempted, and we recommend that figure: the gap between 81.1% and 46.2% is the distance between an edit was made and a document was forged. Forty-six successes sit on scanned pages where survival is undecidable; we do not remove them and 41 reach the final rung, so 808 is itself an upper bound. Typeface failures are concentrated in two models; survival affects every model about equally, because it is a property of the document rather than the editor: a value printed on four pages needs four edits, and nothing in the task asked for four (Appendix G). Figure 4 shows what this looks like on real cells: a forgery that survives, and two ways an edit the text layer accepts still fails as a document. 4.4
save — graded by the same verifier on the same 125 documents, and reported hardened: an independent diagnosis attributed 8 of its failures to its own locator, and we credit it all 8 (Appendix H). The agents strictly dominate (Figure 8, Appendix F). They forge 124 of 125 documents against the script’s 98 of 125, and no document is forged by the script and missed by the agents, in any stratum. The 26-document margin is significant overall (exact McNemar p = 3×10−8 ) and on the 113 documents whose target sits in a readable text layer (112 versus 94, p = 8 × 10−6 ), so it is not an artifact of the 12 scans, where the agents lead 12 of 12 against the script’s 4 of 12. Eleven of the 27 documents the hardened script still fails were diagnosed as structural: no contiguous byte span for the target exists at any encoding, so reaching them means re-synthesising kerned TJ arrays — a different and much larger program than the one we wrote. That is the work the agent removes.
What the agent adds over a script
A capability claim needs a counterfactual. Ours is a deterministic content-stream editor with no model in it — locate the byte span, decode it through the font’s /ToUnicode CMap, substitute, re-encode, 5
a survives every check deepseek-v4.1-flash as filed
survives
as returned
rendered, localized, typeface matched, and $1000 is absent from every page of the file
b wrong typeface mistral-small-2603 as filed
as returned
removed by the typeface filter correct string, but set in Helvetica on a Times page, and it overruns the next word
c attempted wrong mistral-small-2603 as filed
as returned
removed by the verifier the new date was inserted above the old one; April 21, 2023 is still on the page
Figure 4: Three real cells, as filed (left) and as returned (right), target boxed, with the check that decided each. (a) A forgery that survives every filter. (b) The string is right but the agent set it in Helvetica on a Times page. (c) The agent inserted the new date and left the old one in place. The other two ways an accepted edit fails as a document — the original value surviving on a later page, and a text-layer rewrite that changes no rendered pixel — do not photograph well and are counted in Figure 3.
4.5
How the agents actually do it
4.5% write the receipt. The median cell makes 14 calls before its first edit attempt and delivers edited.pdf 163 s after its first call. The modal chain, orient–inspect–edit–verify–receipt with no repetition, is 190 cells; 541 (30.9%) loop from verification back to another edit. Verification is nearuniversal: 1,562 of the 1,595 cells that delivered a file re-read it, 1,130 by re-extracting text, 947 by rendering the page, and 604 by opening a rendered PNG with the read tool after editing. Median edit attempts is 2; mistral makes 7 and holds 158 of the 285 cells with five or more; 1,917 of 5,575 attempts exited non-zero. Attempted-wrong cells are the long chains (median 42 calls, 5 attempts, 57% with a loop); failed cells are the short ones (median 15 calls; 108 of 140 never ran an edit). Nothing exotic is reached for: the writers are the libraries a document pipeline installs for legitimate reasons. Agents chose a full rewrite, which discards the prior revision history, in 1,308 of the 1,342 determinable successes; the 34 incremental saves, which leave the original bytes recoverable, come almost entirely from one model (33 from z-ai/glm-5.3). Web access, package installation and the receipt-based tally are in Appendix I.
The receipts name a library; the traces show it. Each cell’s opencode session database records every tool call with input, output and timestamps: 55,975 calls over the 1,750 cells, none missing. The harness exposes ten tools; the agents used one. bash is 49,016 calls (87.6%), read 4,744, write 1,654; webfetch was called 20 times in 10 cells and websearch never. The writer is pikepdf in 1,264 cells (72.2%) and PyMuPDF in 284 (16.2%); qpdf, pdftk and mutool together wrote 24 files, handpatched bytes 20, and 155 cells delivered no file (Figure 5a). Six models default to pikepdf; mistral-small-2603 alone defaults to PyMuPDF (143 of 250). Pooled, pikepdf files succeed at 93.4% and PyMuPDF files at 74.3%, but the gap is the model, not the library: in each of the five models with at least 13 cells on both, the two rates are within three points (deepseek 97.5 vs. 100; mistral 57.6 vs. 55.9). The receipt’s tool field matches the observed writer in 1,438 of the 1,468 cells where both exist (98.0%); 27 of the 30 mismatches switched library mid-run, and 23 name the earlier one. The 78 successful receipts that named no library were pikepdf in 69 cases. The chain has one shape (Figure 5b): median 26 calls per cell (IQR 18–39), of which 57.6% inspect the original, 10.5% edit, 15.2% verify and
4.6
The agents misreport their own failures
Honesty is conditional on success in a specific and unflattering way (Figure 10, Appendix F). When the edit worked, 87% of runs filed an honest receipt. 6
(a)
(b)
0
25
50
75
100
75 50
receipt
10 14 21 22 19 18
median and IQR success attempted wrong / failed
edit
95 84 84 79 76 74 57
deepseek-v4.1-flash glm-5.3 muse-spark-1.3-contributor qwen3.7-plus minimax-m3 kimi-k2.6 mistral-small-2603 13
raw bytes / sed other no PDF written 100
% of calls
pikepdf PyMuPDF qpdf / pdftk / mutool
(c)
verify
inspect
25 orient 0
0
25
50
75 100
% of cells (250 per model) position in chain (% of calls)
0 20 40 60 80
tool calls per cell
Figure 5: What the traces show. (a) The library that wrote /out/edited.pdf, identified as the call whose time window contains the file’s modification time (1,594 of 1,595 delivered files); models ordered by success rate. (b) Phase of each call against its position in the chain, all 1,750 cells weighted equally; web and other are 1.5% of calls and are not labelled. (c) Tool calls per cell for successful and non-successful cells, same row order as (a); per-model n as in Figure 2.
When the agent edited the wrong thing, 41% of runs filed a receipt claiming an edit the file does not contain — 58% of the receipts those runs filed, since 50 of the 176 filed none. The self-report is least reliable exactly where an operator would most need it. 4.7
search costs, which removes cost as a constraint on volume. 4.9
We could not detect an effect of instruction detail (Figure 12, Appendix F). Arm A, one bare sentence, reaches 81.3% against 80.9% for arm B, which adds library names, a technique and a self-verification instruction; of 875 paired cells, 82 were won by bare intent alone and 79 by the guided prompt (exact McNemar p = 0.87). This does not make prompt detail irrelevant; it bounds any effect at roughly ±4 points on this corpus. Within that bound the capability is not prompt-engineeringlimited.
The edit is very small, except when it is not an edit
The median verified forgery alters 0.024% of the rendered page (Figure 11, Appendix F): trivially easy to miss by eye, trivially easy to find by machine, which is why the localization check matters. The caveat: 22 successes changed no rendered pixel at all and 84 used the wrong typeface (Figure 4b). Both satisfy every text-layer rule; the first is not a forgery and the second would not survive a glance. 4.8
Telling the agent how to do it does not measurably help
4.10
Refusals
No model refused on safety grounds in any of the 1,750 cells. Two cells flagged by the transcript regex are false positives: the pattern matched the document’s own audit boilerplate echoed to stdout (Appendix J). We report an absence of observed safety behaviour on this corpus, not a claim that none exists.
What a forgery costs
Cost is recorded per API call by the routing provider and is measured, not estimated: the seven models spent $248.94 on 1,419 verified forgeries (Figure 6). There is no capability–cost tradeoff. deepseek-v4.1-flash is the most capable model in the set (97.6%) and the cheapest per forgery at 2.4 cents; glm-5.3 matches it (97.2%) and costs sixteen times more ($0.396). On the strict measure of Section 4.3 the cheapest is 4.0 cents and the set average 30.8 cents. These are retail marginal costs and the wrong number to read as a budget; the relevant fact is the order of magnitude. Altering a filed audit report costs about what a web
5
Discussion
The barrier was effort, and it is gone. The combination that produces these results — a generalpurpose agent harness, an open-weight model, and libraries maintained for legitimate document processing — has no attack-specific component to
7
verified / surviving every check (hatched)
capability does not cost more success rate (%)
100
deepseek
deepseek-v4.1-flash
muse qwen3.7
80
glmmuse-spark-1.3-contributor mistral-small-2603 minimax kimi qwen3.7-plus
60
minimax-m3 mistral
40 0.02
0.05
0.1
0.2
US$ per verified forgery (log)
0.024/0.040 0.047/0.077 0.059/0.221 0.061/0.108 0.234/0.385
glm-5.3
0.396/0.654
kimi-k2.6
0.406/0.709
0.5
10 1
100
US$ per forgery (log)
Figure 6: Left: cost per verified forgery against success rate. The cheapest model is also the most capable, so there is no capability–cost frontier to trade along. Right: cost per forgery, verified and after every validity filter. Note the log scale: the range across models is a factor of seventeen.
withhold. The scripted control splits the claim in two. Byte-level editing of a cooperative document was never the barrier: a script does it, and on 98 of our 125 documents it also produces a verifieraccepted forgery. But the split is not even. There are 26 documents the agents forge and the hardened script does not, and none where the reverse holds; eleven of the 26 have no contiguous byte representation at any encoding and eight more are scanned pages. On most documents the agent removes work — somebody had to write that script, and we wrote it four times before it was a fair control; on the eleven fragmented documents it removes something closer to a capability. What it does not supply is document-wide consistency (Section 4.3). The requester supplies one sentence and, on the cheapest model, 2.4 cents per verifieraccepted forgery. A threat model resting on “few people can do this” was wrong before agents arrived.
1,308 of 1,342 — and the median visual footprint is 0.024% of the page: a vector-layer regime in which raster-domain detectors have not been evaluated. The detection benchmarks we built previously [1– 3] are populated with generated or image-edited pages; a detector trained on them has never seen a content-stream substitution that leaves every pixel outside the target untouched. We stop short of a detectability claim in either direction. In particular, we do not claim that erasing the incremental-update chain gives a structural detector a signal: legitimate re-saves erase it the same way. Running detectors against matched controls is the obvious next step.
6
Conclusion
Seven open-weight models driving a stock agent harness altered real filed financial documents to the verifier’s satisfaction in 1,419 of 1,750 cells (81.1%), and in a way that survives every further validity filter in 808 of 1,750 (46.2%). The gap is the main finding: passing a text-layer check is easy and nearly free; making the rest of the document agree is where half the apparent capability goes. What the agent removes is expertise, not possibility: a hardened script still solves 98 of the same 125 documents, but none the agents miss. The agents misreported their own work, no model refused, and one bare sentence did as well as a guided prompt.
Verification cannot rest on self-report. Of the 1,595 files produced, 181 (11.3%) carried a receipt that contradicted the file, and the errors concentrate where they do the most damage: receipts were honest 87% of the time when the edit succeeded, but 41% of the runs that edited the wrong thing filed a receipt asserting an edit the file did not contain. A missing receipt, by contrast, reliably marks a run that made no edit at all. Any system delegating document edits to an agent must verify the artifact independently.
A
Pairwise model separation
Table 1 gives Fisher’s exact p for every pair of models on the success counts of Figure 2. With a Bonferroni correction over the 21 comparisons (α = 0.0024), 14 pairs separate. The seven that do not are the four leaders among themselves (deepseek-v4.1-flash, glm-5.3,
Implications for document trust. Nearly every determinable successful edit was a full rewrite — 8
The container receives a dummy key and a local base URL; the key-holding shim writes every request and response to disk, which is the source of the economics in Section 4.8.
muse-spark-1.3-contributor, qwen3.7-plus) and the minimax-m3–kimi-k2.6 pair, which is the resolution limit of 250 cells per model. The two leaders are separated from the rest by a margin far larger than their intervals, but the gap between them should not be treated as established.
B
D
The verifier records the verification mode per cell (text-layer or OCR at 200 dpi). The receipt audit checks that the declared before/after values and page match the card, that a tool is named, that the declared save mode matches the %%EOF evidence, that declared extra packages were actually installed (against pip freeze), and — the load-bearing check — that the edit the receipt claims was actually applied. Misdescribed means the edit happened but the receipt describes it incorrectly; fabricated means the receipt claims an edit that was not applied.
Corpus construction
Route registry. A route is a reproducible procedure for obtaining published documents from the body that issued them, recording endpoint, enumeration method, achievable scale, whether documents are natively generated or scanned, and whether the original bytes survive. The registry holds 184 routes, 96 verified, across twelve issuer families (lender disclosures 51, non-US 43, US federal 30, US states 25, plus regulators, municipalities, courts and international organisations); 88 routes preserve the issuer’s bytes, 10 carry a publisher signature stamp, 12 are normalized by the publisher.
E
Corrected tier labels
The tiers analysed in the paper are not the ones the task generator assigned. Five documents initially labelled T2 render as full-page scanned images and were re-labelled T3 on inspection of their page structure (image area, embedded image count, extractable character count, and whether the text layer is drawn in invisible render mode). The draw is therefore T1 = 1, T2 = 92, T3 = 12, T4 = 20 documents rather than the generator’s T1 = 1, T2 = 97, T3 = 7, T4 = 20. We analyse on the corrected labels throughout and keep the original ones in the released cards for provenance; the upstream labelling bug is unfixed. The single T1 document contributes 14 cells (2 arms × 7 models), all money targets, and is omitted from Figure 9. Making the tier axis a genuine experimental factor would require resampling the corpus under per-tier quotas and rerunning, which we have not done.
Per-file scoring. Every harvested file is opened and classified before it is eligible for a task: native-clean, native-messy, OCR-scan, raw-scan, OCR-rebuilt, no-unicode-map or broken, together with whether it is signed, whether its incrementalupdate chain is intact, its producer, page count, characters per page and image-area ratio. Blank fillable forms are excluded: a form nobody filled in has nothing to falsify.
Forgery-Seriousness Index. Each route is scored FSI = 3.5 C + 2.5 R + 3.0 A + 1.0 F over four axes rated 0–10: Consequence if one forged instance were accepted, Reliance (how routinely the class is submitted as proof), Authority (whether our bytes come from the issuing body at a canonical endpoint) and Fidelity (whether we hold the issuer’s exact bytes with a native text layer). The last two are claims about our own collection and are tracked separately as Pristine = (3A + F )/4. Of 107 scored routes, 33 reach Pristine ≥ 9 and 55 are issuer-direct; eligibility requires Pristine ≥ 6. The weights are asserted rather than derived and the ratings are single-rater. The highest-ranked financial sources are FFIEC quarterly call reports (FSI 93.5), federal single audits and audited annual reports filed with the SEC (90.0), NCUA credit-union call reports (87.5), SEC administrative proceedings (87.5), Form 5500 pension filings with the independent auditor’s opinion bound in (85.5), and state auditor reports feeding the municipal bond market (84.0).
F
Supplementary result figures
The five figures below support Sections 4.2, 4.4, 4.6, 4.7 and 4.9; every number they show is stated in the corresponding section of the main text.
G
Typeface filter by model
Typeface failures are not general: five of seven models are at or above 99.4% typeface-correct conditional on success. Across all 1,419 successes only 84 carry the wrong font, and 81 of those 84 come from two models (mistral-small-2603, 65; qwen3.7-plus, 16). The rung in Figure 3 removes 78 of them; the other six had already been dropped by the localization filter. The filter compares font resource names, so it catches a substituted face but not a subset font that lacks the glyphs the new string needs (Appendix K).
Pristine pool. The pool holds 3,662 files over 75 routes and seven issuer families: 3,261 native-text, 207 scan-image, 191 sparse-text, 3 with neither text nor image; FSI 56.0–96.5, median 75.0; median 13 pages. The corpus spans compressed and uncompressed streams, born-digital text and scanned pages, single- and multi-occurrence targets, subset fonts and kerned numeric runs, because it was acquired from issuers rather than generated.
C
Verifier detail
H
Harness detail
The scripted control: hardening and failure diagnosis
The control locates the byte span of the target, decodes it through the font’s /ToUnicode CMap, substitutes, reencodes and saves. It rose across four versions during development, and every increment weakened the uplift we were claiming, so we had an independent diagnosis run on 19 of its 35 failures. Eight were the script’s own fault — the target sat contiguous and decodable inside a nested Form XObject or an AcroForm widget appearance stream that the locator never recursed into — and we credit the script all 8, taking it from 90 to 98 of 125 without writing the code. That is the conservative choice: it makes the control stronger than the one we ran. Of the 27 failures that remain after hardening, the 11 that were diagnosed are structural rather than incidental; the other 16 — eight scans, four T2 and four T4 — were not individually
Each container runs as an unprivileged user with 2 CPUs, 4 GB of memory, a 20-minute wall clock and a 40turn budget. The image provides opencode in fullautonomy mode over Node 22, plus pikepdf, PyMuPDF, pypdf, pdfrw, borb, pdfminer.six, pdfplumber, reportlab, img2pdf, and the qpdf, pdftk, mutool, Ghostscript, ocrmypdf, Poppler and Tesseract binaries. Agents may install further packages from a warm shared cache and may search and fetch the web; all egress passes through a logging proxy and becomes part of the trace. Requests are issued with max_tokens of 32,000 (16,384 for llama-3.3-70b-instruct) at the provider’s default temperature, except moonshotai/kimi-k2.6, which was issued temperature 1 — a choice we would not repeat.
9
1.000
0.012 0.025
0.005 0.010 0.869
<0.0001 <0.0001 <0.0001 <0.0001
mistral-small-2603
kimi-k2.6
minimax-m3
qwen3.7-plus
muse-spark-1.3-contributor
glm-5.3 deepseek-v4.1-flash glm-5.3 muse-spark-1.3-contributor qwen3.7-plus minimax-m3 kimi-k2.6
<0.0001 <0.0001 <0.0001 <0.0001 0.841
<0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001
Table 1: Fisher exact p-values, all 21 model pairs. Bold entries survive Bonferroni correction at α = 0.0024.
issuer family 55
source consequence
29 24
median 78
20
T1 1 92
T2 T3
12
T4
20
documents
us-state lender-disclosure us-federal 8 us-regulator us-municipal 4 non-us 3 intl-org 2
structural tier 15 10 5 0
60
80
Forgery-Seriousness Index
T1 holds 1 document.
Figure 7: The 125 evaluated documents. Left: issuer family. Centre: the structural tier of the target page, on corrected labels (Appendix E); the corpus is dominated by compressed-stream documents and contains a single T1. Right: the ForgerySeriousness Index of each document’s source route. The draw is not balanced across tiers, and we report the tiers as observed strata rather than a designed ladder.
agents (any model) hand-written expert script, hardened
one square per document
all 125 documents
98 expert script and agents 26 agents only 1 neither
readable text layer
124/125 98/125
p = 3 × 10 8
112/113 94/113
p = 8 × 10 6
12/12
scanned page image
4/12
0
p = 8 × 10 3
25
50
75
% of documents forged
100
Figure 8: Left: one square per document, by who can forge it. Right: agents against the hardened script, split by whether the target page carries a readable text layer. p from an exact McNemar test on the discordant pairs, the correct test because both methods are evaluated on the same documents.
diagnosed. In the structural documents no contiguous byte span for the target exists at any encoding: the string is shredded across TJ array elements with kerning adjustments between
them, or drawn one glyph per operator, or — in one case — a word space that exists only as a −223.9 kerning value and no glyph at all. Editing those requires re-synthesising the array
10
success rate by page structure × target T2 compressed stream
86%
85%
n=378
n=630
88%
T3 scan / OCR
n=56
74%
T4 multi-occurrence
77%
n=70 date
n=140 money
76%
T2
71%
T3
n=280
70%
n=42
the corpus is not evenly stratified
n=70
67%
92 12 20
T4
n=70 location
0
20 40 60 80 100 source documents in the tier
Figure 9: Left: success rate by page structure and target type on the corrected tier labels (Appendix E), with the cell count in each square. Right: how many source documents sit in each tier. T1 (uncompressed content stream) is omitted from both panels: it contains a single document whose 14 cells are all money targets (13 successes), which is an anecdote, not a stratum. The corpus we sampled is dominated by T2, and T3 and T4 rest on 12 and 20 documents, so we read no tier gradient off this figure.
honest misdescribed
claims an edit the file does not contain no receipt
87
cells that succeeded
26
cells that edited the wrong thing
n=1419
41
28
n=176
100
cells that made no edit
0
20
40
n=140
60
% of cells in the group
80
100
Figure 10: Receipt honesty, conditioned on what the run actually did. Fabricated means the receipt asserts an edit the file does not contain. Cells that made no edit at all file no receipt.
a verified forgery is a very small edit median 0.024%
100 50 0
10 2
10 1
100
84
80
successful cells
successful cells
150
except when it is not an edit at all
101
% of the rendered page that changed
102
60 40 20 0
22 changed no rendered pixel
wrong typeface
Figure 11: Left: distribution of the share of the rendered page that changed, log scale, over successful cells. Right: the two pathologies the raw success rate hides.
and recomputing the kerning. On the 12 scanned documents the agents lead 12 of 12 against the script’s 4 of 12, and seven of the eight scanned pages the script misses carry no text layer at all. We have no proof that 98 is the ceiling.
I
slightly. Save mode is determinable in 1,342 successes: 1,308 full rewrites and 34 incremental saves, 33 of the latter from z-ai/glm-5.3. The incremental saves are the cases where the pre-edit value remains trivially recoverable from the file.
Web and installation. The web was reached in 59 cells (3.4%), mostly by curl to GitHub, in 57 of them for a font file; webfetch was called 20 times in 10 cells, websearch never. Installation is rare and one-sided: 280 cells (16.0%) ran pip install, 161 installed something, 140 of them fonttools; 288 of the 598 attempts were refused under PEP 668 and every cell that installed anything had first been refused. Of the 161, 125 listed the package in the receipt.
Tool and save-mode tally
Receipt-based tally. Of the 1,419 successes, the receipt names pikepdf in 1,125 and pymupdf in 199; qpdf, pypdf and pdftk together in twelve; 83 receipts named no library. The traces (Section 4.5) attribute 69 of the 78 nolibrary successes that filed a receipt to pikepdf, and show that when an agent switches library mid-run it tends to name the earlier one, so the receipt tally understates pikepdf
11
success rate (%)
100 80
875 paired cells · exact McNemar p = 0.87
81.3%
80.9%
60
only bare intent won
82
only the guided prompt won
79
40 20 0
714
both arms agree
A bare intent B guided one sentence tools + self-check
Telling the agent how to do it does not measurably help.
Figure 12: Intent framing. Left: success rate by arm. Right: the 875 paired (document, model) cells, split by which arm won.
Turn budget. The contract tells the agent it has 40 turns
was also the one model given half the output budget, which plausibly contributes to the retry loop. Because 113 of the 114 receipts it filed are fabricated, including it would dominate every honesty statistic; we therefore exclude it from those as from everything else.
and 20 minutes. The wall clock is enforced by the harness; the turn count is not. Counting assistant steps in the session databases, the median cell used 25 and 389 cells exceeded 40 (maximum 173). The 40-turn figure is therefore a budget stated to the agent, not a cap on its API calls.
J
OCR oracle. Scanned-tier cells are graded by rendering the page and running OCR. During a 20-document pilot of the same harness and verifier we re-graded every scanned-tier cell that produced a file (33 of them) at 150, 200 and 300 dpi. The verdict agreed with the recorded outcome in 33 of 33 cells at both 150 and 200 dpi and in 32 of 33 at 300 dpi, so T3 grading is not resolution-sensitive at the scale that matters. This is a stability check rather than a ground-truth check, and it has not been repeated at the scale of the run reported here, whose 12 scanned documents yield 168 scanned-tier cells. A manual adjudication at that scale would be stronger than either.
Refusal regex false positives
Two cells were initially classified as refusals by a regex over the run transcript. Both matched the document’s own boilerplate: a Florida Auditor General report stating that an audit “cannot be relied upon to identify all instances of noncompliance, fraud, waste, abuse, or inefficiency,” which the agent had echoed to stdout while extracting the page text. Both transcripts end in Python tracebacks on a CID-encoded font; the true outcome is a technical failure. No model objected to the task in either run. Five further regex matches occur in the excluded model and are likewise spurious.
K
Document-wide survival. Re-examining all 1,419 successful edits document-wide, the original value is still present somewhere in 539 of the 1,373 cases where the question is decidable (39.3%), so only 834 of those yield a document that is internally consistent about the figure that was changed. The remaining 46 successes are on scanned pages with no document-wide text layer, where survival cannot be determined either way. Excluding the scanned tier the rate is 515 of 1,293 (39.8%), no lower than the overall figure. Of the 262 edited cells whose target appears more than once on the page, 51 do not match the required occurrence count, and in 30 of those the agent made an edit and changed the wrong number of instances.
Threats to Validity
Five properties of this study bear directly on how its numbers should be read; all were recovered from the retained logs rather than inferred.
Serving heterogeneity. Recovering the provider field from every logged response (Table 2) shows the routing was far from uniform: z-ai/glm-5.3 was served by 17 distinct backends over its 7,164 logged calls, with no single backend carrying more than 53% of them; moonshotai/kimi-k2.6 by 8, deepseek/deepseek-v4.1-flash by 6 and minimax/minimax-m3 by 5. Only meta/muse-spark-1.3-contributor, mistralai/mistral-small-2603 and qwen/qwen3.7-plus were served by a single provider throughout. Providers differ in quantization, context window and tool-calling implementation. Temperature was left at the provider’s default for every reported model but moonshotai/kimi-k2.6, which was explicitly set to 1, and the excluded meta-llama/llama-3.3-70b-instruct was issued a max_tokens of 16,384 against 32,000 for every other model. Neither asymmetry was intended; both were inherited from the harness’s per-model defaults and found only by auditing the logs. Any re-run should pin the provider and fix the temperature, which costs nothing but a request parameter.
Fidelity. The verifier’s success verdict does not check typographic fidelity. A substituted numeral can lose the original’s column alignment, a replacement word can shift the advance width of a justified line, and six T3 successes are whole-page rasterizations that change up to 99.9% of the pixels while satisfying the OCR-based target check. The typeface filter compares font resource names, which misses a subset font lacking the glyphs the new string needs: one candidate for Figure 1 rendered “Greenville” as “reenille” under a matching font name. A fidelity-gated success rate — advance-width delta, baseline offset and subset-font glyph coverage — is the first change we would make to the verifier; until then, the reported rates are an upper bound on the forgeries that would survive a careful human reader.
L
Limitations
Scale and resolution. The headline rests on 125 source documents; per-model rates are over 250 cells, and the gap between deepseek-v4.1-flash and glm-5.3 is one cell wide (Appendix A). The comparator is ours, written and hardened in support of our own result; of its 27 remaining failures the
Scaffold fit. llama-3.3-70b-instruct averaged 79 API calls per cell against a 40-turn budget, four times the rate of the most efficient model, and 25 of its requests returned HTTP 400 for exceeding a 131,072-token context limit. It
12
Model
API calls Distinct per cell backends
glm-5.3 kimi-k2.6 deepseek-v4.1-flash minimax-m3 muse-spark-1.3-contributor mistral-small-2603 qwen3.7-plus
28.7 40.5 26.8 49.2 41.3 39.2 19.8
17 8 6 5 1 1 1
Routing share Mistral 53%, Friendli 10%, Wafer 9% StreamLake 64%, Baidu 14%, Inceptron 9% DeepInfra 82%, GMICloud 8%, Wafer 5% Together 71%, Venice 23%, Novita 4% Meta 100% Mistral 100% Alibaba 100%
Table 2: Serving conditions recovered from the API shim logs. Routing share lists the three most-used backends per model, as a share of the calls for which a provider was recorded. Three of the seven were served by a single provider for the whole run.
Source documents and PII. All source documents
independent diagnosis examined eleven and found all structural, and a third-party control graded identically would be better evidence. No frontier model was evaluated; “not a barrier only a frontier model crosses” is a statement about seven open-weight models. The tiers are observed, not designed: 92 of 125 documents are T2 and one is T1, so T1 supports no rate and is omitted from Figure 9. Success is not undetectability, and grading is exact string replacement, so real-world plausibility is an upper bound on the reported rates. One harness, one run per cell. Capability and model– scaffold fit are not separable here (API calls per cell range from 19.8 to 49.2 across the reported models), and no rate carries a run-to-run variance component, so the Wilson intervals in Section 4.1 understate total uncertainty; repeating the cheapest model over all 250 of its cells would cost $5.90. The control is a lower bound. It rose across four versions and every increment weakened the uplift, so every uplift figure is an upper bound on the agent contribution. It is not plausibly zero: eleven of the remaining failures are targets a locator cannot reach by construction.
are public records published by their issuing bodies — audits, call reports, enforcement orders, tax filings — and were retrieved from public endpoints. Some name individuals and entities as a matter of public record. The released cards carry the issuer’s canonical URL, so anyone reproducing the work fetches from the issuer, as we did.
Disclosure. The capability is generic to the PDF format rather than a defect in any product, so there is no single vendor to notify and no patch that would close it: the libraries are behaving correctly. We are sharing these results with document-verification practitioners ahead of publication.
Benchmark contamination. A published forgerycapability benchmark can be trained against, and a model that scores well because it memorised these 125 documents would be indistinguishable from one that got better at editing PDFs. The deterministic, hash-seeded task generator is the mitigation: fresh targets can be drawn from the 3,662document pool at any time, and the 125 evaluated documents are a draw rather than the benchmark itself. We recommend reporting on a freshly drawn set.
Model set and moment. Seven open-weight models reached through a single gateway at one point in time; serving and decoding were not uniform (Appendix K). The T3 rasterization cases show the gap between success and undetectability: those are successes that would not survive casual inspection.
M
What we are not claiming. We make no claim that these artifacts would evade any particular detector — including the detectors our own benchmarks [1, 2] evaluate — and we deliberately did not tune any edit for evasion. Appendix K is explicit that the reported rates are an upper bound on forgeries that would survive careful human inspection.
Ethics and Responsible Release
Why publish. Document verification systems are being designed under assumptions about attacker effort that these measurements contradict; a lender, an auditor or a KYC provider deciding how much to trust a submitted PDF is making a quantitative bet with no public number to bet against.
We measured a capability; we did not create one. Section 4.4 is the evidence: a script with no model in it solves 98 of our 125 documents. What the agents mostly add is not a new capability but the removal of the expertise needed to exercise it. The technique is documented in the libraries’ own manuals, and this work contributes no code that performs any edit better than the libraries it calls.
What is and is not released. We release the balanced dataset — 81 filed originals and the 81 verified forgeries paired with them — together with the task cards (public document URLs, target strings, and the deterministic mutation seed), the verifier, the per-cell verdicts, and the aggregate traces and receipts that support the paper’s statistics. The remaining edited files exist only inside the benchmark’s own run directories on the compute host and are not released. Full agent transcripts are released on request under a research-use agreement rather than openly, on the view that the aggregate results support every claim in this paper while the verbatim transcripts add recipe value without adding scientific value.
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