--- type: Article title: "PDF Mirage: Content Masking Attack Against Information-Based Online Services" resource: "https://www.usenix.org/conference/usenixsecurity17/technical-sessions/presentation/markwood" tags: [article, webseclist-reference, en, usenix-org] generated: by: webseclist-refs/1 at: "2026-08-19T16:43:45+00:00" status: stable stale_after: 2027-08-19 sources: - id: original resource: "https://www.usenix.org/conference/usenixsecurity17/technical-sessions/presentation/markwood" title: "PDF Mirage: Content Masking Attack Against Information-Based Online Services" author: Ian Markwood, Dakun Shen, Yao Liu, Zhuo Lu - id: capture resource: "https://web.archive.org/web/20170821173301/https://www.usenix.org/conference/usenixsecurity17/technical-sessions/presentation/markwood" also_at: - "https://www.usenix.org/system/files/conference/usenixsecurity17/sec17-markwood.pdf" - "https://www.usenix.org/sites/default/files/conference/protected-files/usenixsecurity17_slides_markwood.pdf" authors: - Ian Markwood - Dakun Shen - Yao Liu - Zhuo Lu canonical_url: "" cited_by: - "2016-17.md:105" commit: "" content_sha256: bf295b853378cf09f6247303cf79d028899da313811eb9aef54f7ed07f622412 depth: full depth_reason: default kind: article language: en licence: unknown original_url: "https://www.usenix.org/conference/usenixsecurity17/technical-sessions/presentation/markwood" published: "" publisher: usenix.org publisher_english: "" raw_sha256: 309ba18bca2051c083297858bc963622ce298c1e35d86e90be12744954118e10 retrieved_from: "https://www.usenix.org/system/files/conference/usenixsecurity17/sec17-markwood.pdf" retrieved_kind: live retrieved_utc: "2026-08-19T16:43:45+00:00" slug: usenix-org-pdf-mirage-content-masking-attack-against-information-based-services snapshot: 20170821173301 title_english: "" translation_file: "" translation_of: "" --- # PDF Mirage: Content Masking Attack Against Information-Based Online Services **PDF Mirage: Content Masking Attack Against Information-Based Online Services** - Ian Markwood, Dakun Shen, Yao Liu, Zhuo Lu, usenix.org. - Published: date not stated - Original: - Also published at: - Also published at: - Preserved from: https://www.usenix.org/system/files/conference/usenixsecurity17/sec17-markwood.pdf (live) on 2026-08-19 - Capture timestamp: 20170821173301 - Licence: unknown Rights remain with the original author and publisher. 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PDF Mirage: Content Masking Attack Against Information-Based Online Services Ian Markwood, Dakun Shen, Yao Liu, and Zhuo Lu, University of South Florida https://www.usenix.org/conference/usenixsecurity17/technical-sessions/presentation/markwood This paper is included in the Proceedings of the 26th USENIX Security Symposium August 16–18, 2017 • Vancouver, BC, Canada ISBN 978-1-931971-40-9 Open access to the Proceedings of the 26th USENIX Security Symposium is sponsored by USENIX PDF Mirage: Content Masking Attack Against Information-Based Online Services † † † † Ian Markwood∗ , Dakun Shen∗ , Yao Liu , and Zhuo Lu † University of South Florida, Tampa, FL, U.S.A * Co-First Authors Abstract oped dealing with arbitrary code execution through some allowances made by Adobe to execute JavaScript within We present a new class of content masking attacks the rendering process of a PDF file [1] [2] or from other against the Adobe PDF standard, causing documents to rendering vulnerabilities [3] [4]. These typically allow appear to humans dissimilar to the underlying content ex- data exfiltration, botnet creation, or other objectives un- tracted by information-based services. We show three at- related to the PDF file itself aside from using it as a de- tack variants with notable impact on real-world systems. livery mechanism [5] [6] [7] [8]. We present a class of Our first attack allows academic paper writers and re- attacks against the content integrity of PDF documents viewers to collude via subverting the automatic reviewer themselves, and following this, describe and test a com- assignment systems in current use by academic confer- prehensive defense method against these attacks. With- ences including INFOCOM, which we reproduced. Our out changing the appearance of a PDF, we are able to second attack renders ineffective plagiarism detection alter how several information-based services see it, with software, particularly Turnitin, targeting specific small the following implications: plagiarism similarity scores to appear natural and evade 1. We demonstrate how academic paper writers can detection. In our final attack, we place masked con- collude with multiple conference reviewers, by altering tent into the indexes for Bing, Yahoo!, and DuckDuckGo a paper invisibly to humans, to be assigned to those re- which renders as information entirely different from the viewers by automatic reviewer assignment systems, such keywords used to locate it, enabling spam, profane, or as that used by the IEEE International Conference on possibly illegal content to go unnoticed by these search Computer Communications (INFOCOM) [9] that openly engines but still returned in unrelated search results. publishes its automated algorithm. We simulate this re- Lastly, as these systems eschew optical character recog- viewer assignment system using 100 sample academic nition (OCR) for its overhead, we offer a comprehensive papers and a corpus of 2094 papers from 114 reviewers and lightweight alternative mitigation method. of a past security conference, finding that we can cause any of said sample papers to match with any reviewer. 1 Introduction 2. We show how an unethical student can invisibly alter a document to avoid plagiarism detection, namely Designed as a solution for displaying formatted infor- the dominant market share Turnitin [10], and general- mation consistently on computers with myriad hardware ize methods to target specific small plagiarism similarity and software configurations, Adobe’s Portable Docu- scores to simulate the few false positives such systems ment Format (PDF) has become the standard for elec- typically detect. We illustrate this attack by inducing pla- tronic documents. Academic and collegiate papers, busi- giarism scores, as measured by Turnitin, from 0-100% in ness write-ups and fact sheets, advertisements for print, 10 academic papers without changing their appearance. and anything else meant to be viewed as a final product 3. Lastly, we show real-world examples of mak- make use of the PDF standard. Indeed, there is an ele- ing leading search engines display arbitrary (potentially ment of constancy implied in the creation of a PDF doc- spam, offensive material, etc.) results for innocuous key- ument. End users cannot easily change the text of a PDF words. We have successfully caused Bing, Yahoo!, and document, so most come to expect a degree of integrity DuckDuckGo to index five documents under keywords present in all PDF documents encountered. not displayed in those documents. Attacks are studied and corresponding defenses devel- These systems have in common the need to scrape USENIX Association 26th USENIX Security Symposium 833 PDFs for their content for further processing or search- make perfect sense to the human eye, while the underly- ing within. Online conference paper or other document ing text read by the machine has many substituted words repositories and companies that index the Internet re- which would not make sense to a human reader. This quire text from PDFs so they may be located via search. exploit has the technical challenges of replacing words Natural language processing tools scrape PDFs to dis- of differing lengths (larger and smaller replacements re- cover the topics within, and this information is used in quire different methods) and also constructing multiple several large conferences to assign unpublished work to fonts required for different mappings of the same letter conference reviewers as well as in document repositories (for example, to map the word “green” to “brown” re- to categorize large volumes of works without manual ef- quires two different font mappings for e). A naive de- fort. Finally, plagiarism checkers require text from new fense could check the number of fonts embedded, so in articles for comparison against currently published work Section 4 we design algorithms to minimize the num- to detect impermissible similarity. ber of auxiliary fonts used, in order to avoid detection. Scraping of PDF documents can be done in an au- To evaluate, we construct our own automatic reviewer tomated setting by text extraction tools such as the assignment system reproducing the current INFOCOM PDFMiner package [11]. However, fonts of any name system [9], and show that for 100 test papers, targeting may be embedded in the PDF document, and these tools a specific reviewer is possible by masking 4-9 unique cannot check the fonts’ authenticity. A font is actu- words in most papers and no more than 12 for all tested. ally akin to an encoding mechanism, which maps keys This content masking attack also undermines plagia- pressed on a keyboard to glyphs representing those keys. rism detection. In this case, we need only switch out iso- Without some way to check the validity of fonts in a PDF, lated characters to change plagiarized text to text never which glyphs a font maps keys to is arbitrary. Moreover, written before, while again masking these changes as the humans reading a PDF read the rendered version of what original text to the human reader. In fact, as most pa- a tool such as PDFMiner reads, meaning that machines pers have a small (false positive) percentage of similarity and humans are on opposite ends of this encoding mech- present due to common phrases within the English lan- anism and may be caused to read different information. guage, this method simulates that by varying the number Consequently, the various PDF document scraping en- of characters changed, to simulate the usual small but vironments may be misused through the remapping of nonzero plagiarism percentage. Only one font is required keys to arbitrary rendered glyphs. Using one or more to make this mapping, as the resultant text does not need custom fonts, an attacker may cause a word to be ren- to make sense to the plagiarism detector. Thus, say, all dered as another word by switching the glyph mapping rendered e’s may be represented by some other letter in within the font file, or rather change the underlying text a font that maps that key to the glyph e, and other letters while keeping a constant rendered output. That is to may be changed similarly, building a one-to-one map- say, in a document containing the word “kind” an at- ping covering at most all letters. The challenge is to tar- tacker may force that to be rendered as “mean” with a get a small plagiarism percentage, but accomplishing that custom font mapping k to m, i to e, n to a, and d to n, as we do in Section 5, a single embedded font bearing the so the human now sees “mean” while the machine still name of a popular font will cause no suspicion. sees “kind”; or to avoid human detection an attacker can Finally, search engines and document repositories may change the underlying text to “mean” and use a font with be subverted to display unexpected content also. Here, the reverse mapping to render it as “kind” for the human we may replace the entire text of a PDF without changing to see. The latter tactic subverts aforementioned end ap- the rendered view, with a variety of implications. One plications, while still rendering PDFs in all appearances may hide advertisements in academic papers or business normal to humans. We refer to this as a content masking fact sheets, for example, to spam users searching for in- attack, as humans are caused to view a masked version formation. In this exploit, the attacker should replace an of the content these computer systems read. entire document with the fewest number of fonts neces- To assign papers to reviewers for a conference, several sary, to avoid seeming particularly unusual. This must large conferences employ automated systems to com- be done in a different way than for the topic matching pare the subject paper with a corpus of papers written exploit, due to changing the entire document rather than by each reviewer to find the best match. This matching a few words, so we outline another method in Section 6. is executed upon the most important topics, or keywords, We then test it on popular search engines, finding that found in the paper via natural language processing meth- Yahoo!, Bing, and DuckDuckGo are susceptible. ods. If an author replaces the keywords of a paper with Having enumerated these vulnerabilities, as these sys- those of a reviewer’s paper, a high match is guaranteed, tems eschew optical character recognition (OCR) for its and the two may thereby collude. By creating custom overhead, we offer a comprehensive and lightweight al- glyph mappings for characters, the masked paper can ternative mitigation method in Section 7. While a naive 834 26th USENIX Security Symposium USENIX Association method would perform OCR over the full document, we collections of related words, using supervised learning. instead render the unique characters used within the doc- The probability of a document corresponding to each of ument and perform OCR on these. This font verification the predefined topics is calculated based on how well method has several technical challenges in its implemen- the words within the document correspond to the words tation, due to the number and variety of glyphs within within each topic [15, 16]. font files, and all these issues are overcome in the algo- Topic matching is used within the automation of the rithm we provide. We find it performs at a roughly con- review assignment process for several large conferences, stant speed regardless of document length (a tenth of that such as the ACM Conference on Computer and Commu- for full document OCR at 10 pages), with glyph distinc- nications Security (CCS) or the IEEE International Con- tion accuracy just under 100%, and with 100% content ference on Computer Communications (INFOCOM). masking attack detection rate. These conferences receive many submissions and have many reviewers, and the manual task of finding the most 2 Background Information suitable reviewers for each paper is onerous, so they au- tomate by comparing topics extracted from subject pa- PDF Text Extraction: The Adobe PDF standard con- pers and papers published by reviewers. The authors tains eight basic types of objects, including strings. of [9] execute a performance comparison between LSI Strings house the text in a document, including plain text, and LDA for use in the present (as of 2016) INFOCOM octal or hexadecimal representations of plain text, or text reviewer assignment system, which uses PDFMiner for with some type of encoding [12]. PDF rendering soft- text extraction, finding LSI to work well with the aca- ware treats each string as a series of character identifiers demic papers submitted to that conference. We accord- (CIDs), each mapping to its corresponding glyph within ingly perform our experiments using LSI to determine the font associated with that string via the Character Map the important keywords of each paper, and note that the (CMap) [13]. A series of glyphs is thus displayed. attack functions equivalently using LDA. Text information extracted from PDF files by using Plagiarism Detection: Turnitin, LLC has the domi- tools like the Python package PDFMiner. These tools ex- nant market share for plagiarism detection software. Its tract text by copying the plaintext from all string objects software is proprietary, but current documentation states in a PDF file. Though these tools can extract the font “Turnitin will not accept PDF image files, forms, or port- name for each string as well, a whitelist will not defend folios, files that do not contain highlightable text...” [10], against this attack, as fonts may be given any name. indicating that PDFMiner or some similar internally de- Topic Matching: The exponential growth of human veloped tool is used to scrape the text from PDF docu- knowledge/record keeping and the ease of its access de- ments. We may assume from the lack of support for im- mands an efficient means of providing context-relevant age files that optical character recognition (OCR) is not search results, stemming the research field of natural lan- used, meaning that our proposed attack should succeed, guage processing. This field extracts the specific subject which is proved in Section 5.2. of a document without the need for human classification. Additionally, the Turnitin documentation states that The ultimate goal of useful search results prompts the “All document data must be encoded using UTF-8 char- companion research field of matching keywords to top- acter set” [17]. As mentioned in Section 2, text may have ics which has been tackled by the leading search engines. custom encodings, but here we find they are not permit- Latent Semantic Indexing (LSI) is a popular natu- ted by Turnitin. This disallows any attack where text, ral language processing algorithm for extracting topics gibberish in appearance, is translated via decoding into from documents. The LSI approach infers synonymous legible text. However, no restriction on fonts is in place, words/phrases to be those with similar surrounding con- due to the necessary ability for Turnitin’s client institu- texts, rather than constructing a thesaurus. These de- tions to specify their own format requirements. tected patterns can allow singular value decomposition Document Indexing: Extracting topics from a docu- to reduce the number of important words in a document ment is somewhat of a subproblem to the larger issue of such that it may be represented by a small subset. This document indexing. As information highly relevant to a small subset, of cardinality k, then contains frequency search may appear in a small portion of a document, sim- data for each element, such that the document may be ply relying on the overall topic of every document to in- represented by a dot in k-space. Similarity between doc- fer relevancy to a search may miss some useful results. A uments is easily calculated via their Euclidean distances search engine should do more than simply topic model- apart in this geometric representation [14]. ing to show results for a query. In fact, Google uses more Latent Dirichlet Allocation is a newer popular topic than 200 metrics to determine search relevancy [18], in- extraction algorithm, which is generally speaking a prob- cluding its famous PageRank system of inferring quality abilistic extension of LSI [9]. Topics are generated as of a site based on the number of sites linking to it [19]. USENIX Association 26th USENIX Security Symposium 835 Though documentation is sparse on other search en- ument. Assignment of conference paper submissions to gines such as Bing or Yahoo, Google does host some reviewers is accomplished by finding the highest similar- discussion of its treatment of PDF files. It states that ity between detected topics within submissions and those they can index “textual content . . . from PDF files that within a corpus of reviewers’ papers. Meanwhile, a lazy use various kinds of character encodings” [20] but that paper writer may wish to collude with specific review- aren’t encrypted. “If the text is embedded as images, we ers, know of some more generous to papers, or just think may process the images with OCR algorithms to extract reviewers may be less critical of papers not within their the text” [20], but for our content masking attack, text is specializations. This lazy writer needs to change the pa- not embedded as images, so logically the system would per topic to target a specific reviewer, replacing words not perform OCR. Our experiment finds out for sure for corresponding to the topic of the paper with words com- Google, Bing, Yahoo, and DuckDuckGo in Section 6.2. prising the topic of a paper from the reviewer’s corpus, while being masked as the original words to still make visual sense. We now discuss the challenges for this at- 3 Masking Font Creation tack and methods to target one or more reviewers, and subsequently evaluate the attack efficacy. The content masking attack is facilitated by the ability to embed custom fonts within PDF documents. In fact, having all fonts embedded is a formatting requirement 4.1 Construct Word and Character Maps for the submission of academic papers to conferences. However, no integrity check is performed on those fonts We primarily require a list of original words within the as to the proper correlation between text strings within subject document to change, and a list of words from the the PDF file and the respective glyphs rendered in the target document to which to change these original words. PDF viewer. An attacker may map characters to arbitrary The new words will then be masked to display as the glyphs and alter the text extracted from a PDF document original words using the masking fonts described in Sec- while it appears unchanged to humans in a PDF viewer. tion 3. First, any stopwords within the document should This requires two steps, firstly to create the requisite font be eliminated from consideration. These are common files and secondly to encode the text via these font files. words within the paper’s language, such as “the,” “of,” The first step may employ one of the multiple open “her,” or “from.” Stopwords may be removed by using source multi-platform font editing tools such as Font- existing tools like the Natural Language Toolkit (NLTK) Forge [21]. With this tool, one can open a font and di- Python package [24]. From here an attacker can replace rectly edit the character glyphs with the typical vector the most frequently used words in the subject paper with graphics toolbox, or copy the glyph for a character and the most frequently used words in the target reviewers paste it into the entry for another character. One can then paper. This will result in the most frequently used words edit the PDF file directly with open source tools such in the target paper also appearing in the subject paper, for as QPDF [22], or in the case of manipulating academic a high similarity score as measured by the LSI method papers, quicken the process by adding custom fonts in within the automatic reviewer assignment system. LATEX, and aliasing each to a simple command [23]. We Consider word lists A and B having constituent words employ the latter method for its greater ease. It em- {a1 , a2 , ..., an } and {b1 , b2 , ..., bn } which are in descend- ploys the program ttf2tfm, included with LATEX, to con- ing order of appearance within the subject and target pa- vert TrueType fonts to “TeX font metric” fonts which are pers, respectively. An attacker wishes to replace words usable by LATEX. Two LATEXcode files are supplied by A with topic B and must therefore replace each word [23]: T1-WGL4.enc for encoding, and t1custom.fd for ai within the text of the subject paper with a word easy importing of the font into a LATEXdocument. bi , encoded using some font(s) to render bi the same The second step of choosing how to mask this con- graphically as ai (a word mapping). No other words tent and what in a document to encode with custom fonts should/need be changed. Consequently, the objective is depends on the system targeted, and the technique and to construct a mapping between the letters of each bi evaluation for each of the three scenarios introduced in and ai (a character mapping). If ai and bi are character Section 1 appears in the following three sections. arrays {ai [1], ai [2], ..., ai [pi ]} and {bi [1], bi [2], ..., bi [qi ]}, then the attacker should construct a masking font such that the character bi [1] maps to the glyph ai [1], bi [2] to 4 Content Masking Attack Against Con- ai [2], etc. We may consider this analogous to a map data ference Reviewer Assignment Systems structure, where bi [1] is a key and ai [1] its value, and so on. Two challenges naturally arise in constructing the As learned in Section 2, topic matching works from required character mappings: groups of words constituting the main topic of the doc- One-to-Many Character Mapping: From the brief 836 26th USENIX Security Symposium USENIX Association example in Section 1 of changing the word green to Original Masked brown, we know that in terms of a map data structure Text Text there is a collision for the key e and the values o and blank w, such that an attacker will require two masking font (clearing font) “maps” to render green as brown. The first challenge is to Favorable Mapping minimize the number of fonts required in the document, so as to avoid suspicion, while fully switching topic A for B. This problem is not delimited by word: some character mappings may be reused in the same or other Unfavorable Mapping words, and many may not. Additionally, changing all of the words in A to those in B may be unnecessary, which Figure 1: Handling the word length disparity challenge. also impacts the number of one-to-many mappings and Each box represents a character. resultant number of required font files. If fewer words must be changed while ensuring the required similarity between papers, fewer fonts may be required, and a naive Algorithm 1 Build Character Map font count threshold defense will be less effective. Input: subject paper s, target paper t Word Length Disparity: Further, the lengths pi and Output: character mapping C : B → A, encoding fonts qi of words ai and bi may differ, causing ai to be longer F = { f1 , f2 , ..., fx } than bi or vice versa. If pi > qi , to render bi as ai , a font 1: A ← top k topic words of LSI(s) file entry is necessary for the letter bi [qi ] mapping to the 2: B ← top k topic words of LSI(t) last pi − qi + 1 letters of ai . Several additional fonts may 3: C ← empty character map be necessary if some bi ∈ B have the same last character. 4: for i ← 1 to k do Thus, we define a favorable keyword mapping as a word 5: pi ← length(ai ) mapping bi → ai such that pi < qi . In this case, only 6: qi ← length(bi ) a single clearing font is needed, wherein all characters 7: if pi < qi then . favorable mapping map to a blank glyph of no width. Figure 1 illustrates 8: for j ← 1 to pi do handling favorable and unfavorable mappings. In prac- 9: C ← C ∪ {(bi [ j], ai [ j])} tice, a blank glyph of no width is in fact a single dot, of 10: for j ← pi + 1 to qi do width (and height) equal to the smallest unit of measure 11: C ← C ∪ {(bi [ j], 0)} / within a font drawing program. In contrast, an i is 569 12: else if pi > qi then . unfavorable mapping units wide (and a w is 1500 units wide), so this dot will 13: for j ← 1 to qi − 1 do not be rendered at all. And because this clearing font has 14: C ← C ∪ {(bi [ j], ai [ j])} all letters map to no-width blanks, it will be the only ad- ditional font required if ∀i, pi < qi , hence its favorability. 15: rest ← combine {ai [qi ], ..., ai [pi ]} 16: C ← C ∪ {(bi [qi ], rest)} 17: else . equal word length 4.2 Matching One or More Papers to One 18: for j ← 1 to qi do Reviewer 19: C ← C ∪ {(bi [ j], ai [ j])} 20: x ← largest number of key collisions in C Mapping of words from B to A is by their original de- 21: temp ← C scending order of frequency within the target and subject 22: for i ← 1 to x do . build fonts papers, respectively. Algorithm 1 shows the overall en- 23: fi ← empty font coding process and begins by running the LSI model on 24: for each c ∈ C do the subject and target papers, then constructing a map be- 25: if value in c is 0/ then tween characters in k of the topic words returned. Then, 26: C ← C \ {c} the mapping is added to C for each character, for each 27: use clearing font for key in c word of B, to the corresponding character(s) in the cor- 28: else if no key collision between c, fi then responding word of A. Here, comments (Lines 7, 12, 29: C ← C \ {c} 17) indicate the steps taken for favorable and unfavorable 30: fi ← fi ∪ {c} mappings and the case when both words are of the same length. Finally at Line 22, the mappings in C are bro- 31: F ← F ∪ fi ken up into collections to be made into custom masking 32: C ← temp fonts, with the exception of those characters from favor- 33: return C, F able mappings which map to null, for which the previ- ously introduced single clearing font is used. Resulting USENIX Association 26th USENIX Security Symposium 837 from this algorithm are fonts to be used for each charac- Matching one paper to one reviewer: The automatic ter of the words in B to mask them as the words in A. If reviewer assignment process compares a subject paper the attacker has multiple papers under submission, this with every paper from the collection of reviewers’ papers process may be repeated independently for each paper. to gather a list of similarity scores. The reviewer with the highest similarity score is assigned the paper to judge (if available). We therefore aim to change a testing paper 4.3 Matching One Paper to Multiple Re- topic to a training paper topic, and to examine how well viewers this works with all papers. For each such pair of papers, then, we replace the frequently appearing words A in the For a better chance at cheating the peer review process testing paper with those frequently appearing words B and to collude with multiple reviewers, the content mask- in the training paper via Algorithm 1. We test the topic ing attack can be adapted to split up the masked words matching of each of the 100 testing papers against our among two (or more) different lists of frequently used training data to see what is required to induce a match. words. Instead of mapping between word lists A and B, For each pair of training and testing papers, we re- the attacker will map between A and B and A and C, such place important words in the testing paper one by one, to that a1 will be replaced with b1 part of the time and c1 the see how many replacements are needed to make that pair rest of the time, and so on. The method is otherwise the the most similar. Figure 2 illustrates this iterative pro- same as shown in Algorithm 1, but has its own challenge. cess for one example training/testing paper pair, showing Intuition would suggest replacing a1 half of the time resultant similarity scores. The box plots show where with b1 and half of the time with c1 . However, the re- the greatest concentration of the 2094 similarity scores quirement for the attacker’s paper to be the most similar dwell, while red pluses show outliers. The blue stars of a large number of papers to a reviewer’s paper and which emerge to the top correspond to the similarity also the most similar of all others to another reviewer’s scores between the testing paper and the target training paper is quite stringent. The intuitive method fails as paper. Figure 2 shows a clear separation of that similarity the similarity score for one target reviewer will be high score from the rest after replacing 9 words, meaning that enough but the other too low. So we use an iterative re- for this pair, content masking all appearances of those 9 finement method which tunes the replacement percent- unique words in the testing paper will result in its assign- ages according to the calculated similarity scores until ment to the reviewer who wrote that training paper. they are both the highest among their peers. This is gen- Performing this process for all 100 testing papers, we eralizable to more than two reviewers, by refining the compile the results into Figure 3, which displays the cu- percentages proportionally according to the successive mulative distribution function (CDF) for the number of differences in similarity scores between the subject pa- words requiring replacement. Evidently, all 100 papers per and each of the target papers. We match one paper to may be matched with the target with 12 words or fewer three reviewers in Section 4.4, the typical number of re- masked. The sharp jump appearing from 4-9 words indi- viewers to which papers are assigned (barring contention cates that most papers can be successfully targeted to a in reviews, which would not happen during collusion). specific reviewer masking between 4 and 9 words. The font requirements for replacing these words is then rep- 4.4 Experiment resented in Figure 4. A majority of papers require 3 or fewer masking fonts, while almost all of them need only We have built a conference simulation system reproduc- as many as 5. This is a comparatively small number and ing the INFOCOM automatic assignment process de- should go unnoticed among the collection of fonts nat- scribed in [9]. We imported into this system 114 TPC ural to academic papers. For example, this paper has members from a well-known recent security conference some 19 embedded fonts, between bold/italic variants, as reviewers, and downloaded a collection of each of fonts used in figures, and one picture font used in Table these reviewers’ papers published in recent years. In to- 1. tal, this comprised 2094 papers used as training data for Matching multiple papers to a single reviewer: the automatic reviewer assignment system. For testing Should an author wish to have multiple submitted papers data, we also downloaded 100 papers published in the all assigned to a target reviewer, the author may simply greater Computer Science field. Our experiment, then, repeat the content masking process on each paper. While is to test the topic matching of the test papers with the in the previous case we find that an average of 3 or 4 training papers, via our content masking attack. Follow- fonts is necessary to make a single test paper sufficiently ing are evaluations of the content masking attack match- similar to the target training paper, that needs not directly ing one paper to one reviewer, multiple papers to one translate to 3 or 4 fonts per paper with multiple papers. reviewer, and one paper to multiple reviewers. Some fonts may be reused among papers, resulting in 838 26th USENIX Security Symposium USENIX Association 0.8 1 1 0.6 0.8 0.8 Similarity 0.4 0.6 0.6 CDF CDF 0.4 0.4 0.2 0.2 0.2 0 1 3 5 7 9 11 13 15 0 0 0 5 10 15 2 4 6 8 10 Number of words masked Number of words masked Number of masking fonts Figure 2: Similarity scores relative Figure 3: Word masking require- Figure 4: Masking font require- to amount of words masked. Blue ments for all 100 testing papers. ments for all 100 testing papers. stars show the desired matching. 100 5 Content Masking Attack Against Plagia- Number of papers 80 rism Detection 60 While a method similar to the topic matching subver- 40 sion technique just outlined may be used to hide plagia- 20 rism, fewer requirements constrain the plagiarist than the 0 lazy author targeting a specific reviewer in a conference. 0 50 100 Specifically, an attacker needs only make the underlying Number of masking fonts text different than the rendered, plagiarized text. The un- Figure 5: Masking font requirements for matching from derlying text does not need to be actual words, and so 1 to all 100 testing papers to a single reviewer. only one font is needed, ensuring the naive defense of limiting fonts is defeated. This scrambling font is just a random scrambling of the characters. Each original letter 0.8 is replaced with the letter which displays as the original. Resulting is a human-legible PDF document which ap- 0.6 pears as gibberish to Turnitin and necessarily has a sim- Similarity ilarity score of 0%. Details and options for this method 0.4 are below, followed by an evaluation of each option. 0.2 0 5.1 Targeting a Specific Plagiarism Score 1 10 20 30 40 50 60 70 80 90 Number of words masked Because Turnitin is a similarity checker, not a plagiarism detector, it relies on the human factor to actually detect Figure 6: Similarity scores relative to amount of words plagiarism. Turnitin informs the individual with grad- masked, between a paper and three reviewers. Blue ing duties of any pieces of similar prose, which naturally stars, black circles, and green triangles show the desired arise due to the plethora of written work in existence and matchings. the human tendency toward common patterns and figures of speech. It is unlikely then, and would stand out to the grader, that a submission would have 0% similarity with anything ever written. We offer and evaluate two fewer overall fonts used. Figure 5 confirms this, show- methods an attacker can use to target a specific (low but ing a trend more logarithmic than linear. non-zero) similarity score and more likely go unnoticed. Matching a paper to multiple reviewers: Finally, we By Letter: Here, the attacker begins with a scram- evaluate the iterative refinement method to split masked bling font and removes characters from being scrambled words among three reviewers’ papers as discussed in successively until a target percentage of the text is not Section 4.3. Figure 6 shows that the similarity scores being replaced. Intuitively, this small target percentage for the three target reviewers (blue star, black circle, would then appear plagiarized, yielding a credible simi- and green triangle) consistently increase; after some 70 larity score. This may be done in a calculated fashion us- words masked, the subject paper is more similar to the ing the known frequency of usage of letters in the English three target papers than any others. (or other) language. The letters may be listed by their USENIX Association 26th USENIX Security Symposium 839 1 curve that is too steep to be manageable for selecting a Frequency descending Random replacment small range of similarity scores. In contrast, the other 0.8 Letter usage descending two methods are very suitable for comfortably picking a Similarity 0.6 specific range. Any probability between 17% and 20% will net a similarity score in our desired 5-15% range in 0.4 the case of randomly chosen masking. When words are 0.2 replaced in order of their frequency of appearance, the 5- 15% range may be achieved by replacing anywhere be- 0 0 0.1 0.2 0.3 0.4 tween 20 and 40% of the words, offering a very wide Percentage range of safety for the plagiarist. Figure 7: Effects of the percentage of text changed upon plagiarism similarity scores for 10 sample documents. 6 Document Indexing Subversion The final direction of this attack is against search en- frequency in ascending or descending order (we evaluate gines, whether for the entire web or for small document both) and then excluded from scrambling in that order repositories or websites. Websites can implement a sim- until the target percentage of unaltered text is reached. ple search returning pages housing the query text, or they By Word: This method is similar to the previous, but can use custom search engines offered by Google [25] or instead of leaving some characters unscrambled in the Yahoo! [26]. Microsoft Bing also offers its API [27]. custom font, the attacker leaves some words unaltered by As small sites are unlikely to have a more sophisticated not applying the custom scrambling font to them. Here, search mechanism than the leading search engines, we words within the document may be listed in frequency target and demonstrate our attack against these. of appearance, ascending or descending, and excluded from the scrambling font in that order (we again evaluate 6.1 Method both). We also consider changing words at random with a probability targeting some similarity score. This method We here consider modifying the entire content of a PDF may be more effective for an attacker in the long run, if to render as something else. Both the underlying text Turnitin implements a requirement that some percentage extracted by PDFMiner (or otherwise) and the rendered of words be found in a dictionary, English or otherwise. text should make sense in this case, so that an individual In that case, this attack may be augmented by the previ- searching for certain terms will be caused to find a PDF ously described method of replacing real words for other holding those words but displaying something entirely real words rendered as the originals. different. This results in a more extreme version of the one-to-many character mapping challenge from the at- tack against topic matching. Instead of masking a small 5.2 Experiment finite number of words, we now examine masking the en- We use 10 already published papers retrieved from the tire content. However, this is facilitated by the realization Internet and mask the content in varying degrees to see that these masks are not necessarily delineated by spaces the effects on Turnitin’s returned similarity scores. We as before; the attacker can treat the entire document as vary the amount a scrambling font is applied to the text a single word to be masked. It consequently encounters according to the previously described methods and up- the word length disparity challenge, to treat the variation load the resultant papers to Turnitin. Again, we target a in length between real and rendered text, but only once. specific range of similarity scores, between 5% and 15%, Nevertheless, the strategy of adding new fonts, ad hoc, such that a human grader is unlikely to suspect foul play. to cover each new mapping quickly balloons out of con- Figure 7 plots the three methods. “Frequency descend- trol, in terms of the attacker needing to keep track of what ing” refers to the method of masking words in the order mappings appear in what font. The number of fonts will of their frequency of appearance in the document, while increase with the number of characters to be masked, to “Letter usage descending” refers to masking letters by an upper limit of every character needing a map to every their frequency of usage. Ascending order proved un- other. Considering (for English) upper and lower case wieldy in both cases and not worth displaying. Finally, letters, numbers, and common punctuation (22 symbols, “Random replacement” refers to the method of iterating dependent upon count), all 26 + 26 + 10 + 22 = 84 char- over all words and masking them with a probability of acters must each map to the other 83 different characters, 1-100% in increments of 1%. These are all plotted in as well as themselves for those cases which a character terms of the percentage of text changed. Masking let- and its mask are the same. This requires 84 fonts and ters by their frequency of usage results in a similarity represents 842 = 7056 mappings. Code can certainly be 840 26th USENIX Security Symposium USENIX Association Search Engine Indexed Papers Attack Successful Evades Spam Detection Not Later Removed Google 3 7 7 7 Bing 3 3 3 3 Yahoo! 3 3 Flagged / Cleared 3 DuckDuckGo 3 3 3 3 Table 1: Results of content masking attack on search engines. written to automatically construct all these mappings, but to make this more efficient, we offer an alternative - 84 fonts, in each of which all characters map to one masking character. For example, in font “MaskAsA” character a maps to a, b to a, 4 to a, ! to a, etc. To mask a document as another, the attacker may simply apply fonts, charac- ter by character, that correspond to the desired mask. At the end of the documents, the three end behavior options presented as part of Algorithm 1 and illustrated in Figure (a) Gibberish paper 1 function here as well, to handle the length variation. 6.2 Experiment To demonstrate the efficacy of this attack, we obtained a handful of well-known academic papers, masked their content, and then placed them on one author’s university website to be indexed by several leading search engines. For this simple proof of attack, we only used one mask- (b) Bing result for the gibberish paper ing font which scrambled the letters for rendering. The resulting papers have legible text that renders to gibber- ish, meaning that if they can be located by searching for that legible text, the search engine is fooled. We submitted the site housing these papers to Google, Bing, and Yahoo! and searched for them some days later. Search engine DuckDuckGo does not accept web- site submissions but we searched there as well. Table 1 (c) DuckDuckGo result for the gibberish paper lists the results of our content masking attack on these search engines. “Indexed Papers” indicates the search engine listed the papers in its index. “Attack Successful” means they are indexed using the underlying text, not the rendered gibberish. After a successful attack, the pa- pers may later be put behind a spam warning or removed from the index, as shown in the last two columns. We (d) Yahoo! result for the gibberish paper found similar results for each of the 5 papers tested: that Bing, Yahoo!, and DuckDuckGo all indexed the papers according to the masked legible text, and none removed them later (at time of writing). Yahoo! did mark them as spam after two days but confusingly some days after that removed the spam warning. Figure 8 illustrates this for one of tested paper. The (e) Temporary Yahoo! spam warning masked paper is shown in Figure 8a and contains no ren- dered English words beyond what is shown. Figures 8b, Figure 8: Results of the content masking attack against 8c, and 8d show the search results for the legible underly- popular search engines. The attack was not successful ing text, and Figure 8e shows the spam warning appear- against Google. ing days later but later disappearing. Each query was USENIX Association 26th USENIX Security Symposium 841 appended with “site:XXX.edu” to isolate the university OCR can distinguish and how to handle those it can’t. website where they are hosted for this proof of concept. Theoretically, OCR may mature to the point where it can Interestingly, Google indexed the papers, but accord- distinguish any sort of accent mark over normal letters, ing to the rendered gibberish, not the underlying text. any characters used in languages other than English, and This indicates, of these four engines, only it performs any additional special characters used in typeset mathe- OCR on PDF files it indexes rather than extracting the matics, etc., and some OCR software may be currently text through PDFMiner or the like. After two days, the in development working on a subset of these problems. papers were removed from Google’s index, before the However, we aim to provide a defense method readily in- authors obtained screenshots. We conclude that Google tegrable into current systems. Additionally, such an ad- has a robust defense against the content masking attack, vanced software will likely incur overhead beyond that while the other three engines remain susceptible. of a current OCR package to achieve the requisite preci- sion, where our solution must be sufficiently lightweight to fit within systems where full-document OCR has not 7 Defense Against Content Masking been applied due to computational complexity. We de- As intoned through this paper, Optical Character Recog- fine a normal set of character codes as those represent- nition (OCR) is able to move the text extraction process ing upper and lowercase English letters, numbers, and from targeting the underlying text to the rendered ver- common punctuation, which English OCR packages tar- sion, preventing this masking attack. OCR is required get, and then we check if the extracted character codes for print documents scanned to PDF, but for documents appear in this normal set or not. A letter in the normal with rendered text, system designers have been loath to set appearing as something other than itself is evidence use OCR in lieu of PDFMiner or its ilk. OCR is far more of the content masking attack, as is a letter outside the complex and requires more processing time than simply normal set having the glyph of one inside. OCR is per- running the PDF file through a lightweight parser to col- formed on all used characters in the font, as previously lect its strings. We propose here a lightweight font veri- mentioned, and those within the normal set are required fication method that enables the use of OCR in a highly to have the correct respective glyph, while those outside efficient way to prevent the content masking attack. The the normal set are constrained not to have a distinguish- intuition is simple; we render each character in the fonts able glyph (i.e. one appearing in the normal set). embedded in the subject PDF file and then perform OCR The third issue arises with the fact that many special on those characters rather than the rendered PDF file it- characters have high similarity with normal characters, self. Where an academic paper may be some 50,-75,000 especially for those fonts in common use which have characters, the fonts embedded therein usually contain at many thousands of available characters. If one such spe- most just a couple hundred characters. cial character is used legitimately in the text, the scheme Challenges and Technical Details: While the intu- just described will flag it as a content masking attack ition is simple, some challenges arise in its realization. due to its similar appearance with a normal set character. First, while most PDF generation tools will embed only Worse, common OCR tools available presently will con- those letters used in the document, it is possible through flate characters which humans can easily tell apart but Adobe InDesign, as one example, to embed the whole for which the software is not precise enough to do so. font. Some fonts accommodate many characters used in For example, it is easy to tell visually that π and n are many other languages, and the upper limit on font char- different characters, but not by common OCR tools. acter capacity is 216 = 65, 536 because characters have Font Training Step: We therefore introduce a training a two-byte index. Clearly, performing OCR on a font of step, wherein OCR is performed on the font and lists of that size will be equivalent to performing OCR on an aca- intersections compiled. When we perform OCR on each demic paper in terms of computational overhead. Conse- represented character and the detected glyph for a spe- quently, we scan the document to extract the characters cial character but appears like a normal letter, we check used, and only render those characters (in their respective the list of characters similar to that normal letter. If the fonts) for OCR verification. This requires iterating over special character appears on that list, we recognize that it the entire document, but the overhead introduced here is may be valid and that we cannot know if it is being used much less than with full-document OCR, as the process legitimately or as part of a content masking attack. As just builds a list from the series of character codes rather the purpose of the content masking attack is to disguise than executing image processing techniques on all char- the visually rendered text as some other text for the com- acter glyphs. OCR is then performed on the series of puter to see, we simply replace the extracted character character codes used in each font only. code for this letter as the normal letter it looks like, and Second, the existence of many special characters pass this on to the end application. If content masking within a font prompts the question of what characters is occurring, the rendered text is sent to the plagiarism 842 26th USENIX Security Symposium USENIX Association detector, reviewer assignment system, etc., thwarting the Algorithm 2 Extract Rendered Text attack. Otherwise, the string in which this special charac- Input: font list F = { f1 , f2 , ..., f p }, normal character ter appears is with high probability not an English word index set N = {n1 , n2 , ..., nq }, special character in- and would not be useful to the end application anyway. A dex set S = {s1 , s2 , ..., sr }, document character list reviewer assignment system or plagiarism detector will D = {d1 , d2 , ..., ds } not make use of mathematical equations when assigning Output: extracted text T = {t1 ,t2 , ...,ts } reviewers, as these are not discernible words, so if πr2 is 1: Unique character index/font map list U = 0/ extracted as nrz , no loss of function is suffered. 2: for i ← 1 to s do 3: if di ∈ / U then This training solution prompts one further issue, 4: U ← U ∪ (di , FONT(di )) which is that different fonts will need to be trained in- 5: m ← |U| dependently as their nuances cause different sets of char- 6: OCR-extracted character index set O = acters to appear similar. For the reviewer assignment and {o1 , o2 , ..., om } plagiarism detection problems, we know a limited num- 7: for i ← 1 to m do ber of fonts should be used, due to academic formatting 8: oi ← OCR(ui ) requirements favoring a small set of fonts. Nevertheless, 9: f ← ui . f ont for other applications, such as search indexing, the only 10: L ← list of similar character lists {l1 , l2 , ..., lv } limit on the number of fonts that can be trained is that for f those fonts must be legible enough for an OCR tool to 11: if ui .index ∈ N then parse. These lists do not occupy too much space; for ex- 12: if oi 6= ui .index then . Attack Detected ample our lists for Times New Roman and Arial fonts are 13: break 29.4KB and 36.2KB, respectively. This database com- 14: else if ui .index ∈ S then piled, the OCR tool will be used to discern the real name 15: if ui .index ∈ loi then . Attack Possible of each font used in the document, to counteract the prob- 16: ui ← oi lem mentioned early in this paper, that an attacker may 17: else . Attack Detected name a font anything desired. Open source OCR tools 18: break such as Tesseract OCR [28] provide this functionality. 19: T ← Apply modified U to D 20: return T Font Verification Overview: The training process be- gins by gathering a collection of fonts and training the Font Verification Performance: The implementation system on each. For each character in a font’s normal set, for this defense method is written in Python and employs all special characters are tested for OCR similarity, and PDF-Extract [29] to extract font files from PDFs, tex- any identified as similar are added to the list for that nor- tract [30] to extract the text strings, and pytesseract [31], mal character. Testing a new PDF file is outlined in Al- a Python wrapper for Tesseract OCR [28]. The alterna- gorithm 2, wherein the list of characters and their fonts is tive to our font verification method is to perform OCR reduced to unique combinations of those attributes, and on the entire document, so we use Tesseract OCR for each then tested with OCR. Content masking attacks are this purpose also for a fair comparison. This comparison detected in lines 12 and 17 when the underlying char- will illustrate not only that our method detects/mitigates acter index is a normal character other than the OCR- the content masking attack as well as the naive full docu- extracted character or when the underlying character in- ment OCR method, but that it performs far better in sev- dex is a special character that does not appear in the simi- eral scenarios common to PDFs both in and out of the larity list for the OCR-extracted character. In these cases, presence of our content masking attack. this pseudocode exits to notify of the attack, though other First, we compare the performance of the two meth- behavior could be inserted here. This protects all end ods with differing amounts of masked content. We gen- applications, except in the attack against plagiarism de- erate 10 PDF files with masked characters varying from tection in which the attacker replaces normal characters 5-20% in frequency of appearance, and apply both meth- with special characters similar in appearance. That spe- ods to each of these file. The results are shown in Fig- cific attack is identified as possible at line 15, in the case ure 9 and show a distinct benefit to our font verifica- that the underlying character is a special character which tion method compared with the traditional full document does appear in the similarity list for the OCR-extracted OCR. Here, detection rate refers to the correct extraction character; in this case all instances of this character in the of rendered text and the consequent ability to prevent the text extracted from this file are replaced with the OCR- content masking attack from occurring. For full docu- extracted character for use in the end application. ment OCR, we generate 10 PDF documents with no con- USENIX Association 26th USENIX Security Symposium 843 50 Full document OCR Detection rate (%) Detection rate (%) 100 100 Font verification 40 Seconds 50 30 90 20 0 Full document OCR 80 Full document OCR 10 Font verification Font verification 0 20 17 14 11 8 5 2 4 6 8 10 5 10 15 20 Percentage of masked characters Number of pages Number of pages Figure 9: Attack detection under Figure 10: Attack detection on Figure 11: Attack detection time varying degrees of attack. PDFs of different sizes. relationship with PDF size. tent masking and measure the error in character recog- the previous experiment, additional pages of text steadily nition, and then we use this error as a threshold, such allow more masked text to go unnoticed. The font verifi- that the attack is flagged for one of the content masked cation appears to be 100% throughout, but actually dips PDF files if it is determined to have a larger difference to 99.8% halfway through. Our method is not immune between characters and their glyphs. That threshold was to the errors inherent to OCR as it also uses OCR, but its measured at 7%, and more than 20% of characters had more judicious approach minimizes those errors. In this to be masked before the full document OCR method de- case, OCR is confusing the ’;’ and ’:’ characters; these tected the content masking attack (after this, detection are rare but eventual in prose. was 100%). The attack is considered detected by the Finally, we demonstrate the performance gain of our font verification method if Algorithm 2 flags it or the font verification method over the full document OCR edge case approach we take of replacing special char- method, on 20 PDF files ranging from 1-20 pages in acters that look like normal letters with those normal let- length and having a 30% distribution of masked char- ters will enable the end application (plagiarism/spam de- acters. In Figure 11, the full document OCR method in- tector) to process the text properly and thereby flag the creases linearly with pages added while the font verifi- attack. In all cases, our algorithm detected the attack or cation method unsurprisingly remains largely static, in- constructed the proper English words required by the end creasing by roughly a second compared to the 45 expe- application to detect it. rienced by the full document OCR method. In all, our The disparity here between the methods’ accuracy in method requires about 6 seconds to check a 20 page doc- the 5-20% character masking range has a few aspects in- ument, rather than 50 seconds, using one core on a laptop volved. Fewer masked characters will appear in a sparser processor (Intel i7 at 2.7GHz). This provides far better distribution, which make them less visible among legit- scalability for the target systems than the alternative, and imate characters. OCR is affected by the distance be- is easily applied to current systems without requiring up- tween characters and the resolution of the image, among grades. other things, which we can control in the case of font verification but which are not controlled when perform- 8 Related Work ing OCR over an entire document. We can generate an optimal image of all relevant characters, check their va- Most exploit research targeting the PDF standard has lidity, flag detected attacks, and in the case of special been in bugs surrounding various programs rendering, characters which appear identical to normal letters, re- displaying, exporting, or otherwise handling PDF docu- place them with those normal letters for proper use in the ments. The not-for-profit MITRE Corporation lists in its end application. Common Vulnerabilities and Exposures (CVE) collec- We also analyze the effects of document length on the tion 431 entries involving the keyword “PDF” and having detection rate for each method, by comparing their re- to do with these external programs [5]. These allow for sults on 10 PDF files ranging from 1-10 pages in length arbitrary code execution on the host computer and all the and having an even 30% distribution of masked charac- associated security risks [6], including establishment of ters. Figure 10 illustrates that while the font verification botnets, data exfiltration, and other high-impact security method is almost perfectly static, full document OCR issues. They are, however, limited to basic hacking-type gradually performs more poorly, reaching 14% misde- exploits, zero-days chased by patches, and the PDF itself tection by page 10. The aforementioned OCR error rate is essentially a vehicle for the hack [7]. These attacks are explains this problem, where while 30% masked charac- not thematically novel, and the patches indeed follow the ters is above the required 20% to guarantee detection in zero-days with reasonable speed [8]. 844 26th USENIX Security Symposium USENIX Association Similarly, some exploration has been performed on Section 2 introduces the Character Map (CMap), the JavaScript execution ability within the PDF standard. through which letters are mapped to entries within fonts, When abused, this too allows for arbitrary code execu- ultimately displaying the associated glyphs. During our tion. Security researcher Didier Stevens offers a series literature search, we found a work [13] from a social of blogs discussing how to misuse this JavaScript exe- science journal of Assessment & Evaluation in Higher cution, including how to encode the strings involved to Education which touches on a similar topic from a non- create polymorphic malware resisting simple signature- scientific stance. [13] discusses how the CMap can be based antivirus products [32]. Some research finds altered to make letters map to different characters within that writing polyglots (code valid in multiple languages) a font. In this way, plagiarism detection can be fooled within PDFs can expose security concerns depending on by mapping to obscure characters whose glyphs are sim- what language the reader uses to interpret the code [2]. ilar in appearance to those for the typically used charac- Successive updates to the PDF standard implement mea- ters. After devising our attacks, we discovered this work sures to block certain functions, such as reaching out to also contains cursory mention of the ability to modify the the Internet, placing their function behind a confirmation glyphs within a font, but does not explore this possibility window for the user to view [12]. Additionally, most cur- or demonstrate its practicality as we do. We evaluate new rent antivirus products offer real-time protection using methods to target specific similarity scores such that the heuristics that can detect potentially malicious behaviors resultant PDF does not appear unnatural with a 0% sim- despite simple code obfuscation. ilarity score. Further, we show how these custom fonts Some academic research regarding PDF security ana- can be used to subvert conference reviewer-assignment lyzes the JavaScript being executed to verify safety. One systems and search indexing, developing new and dis- work analyzes a set of static features extracted from the tinct attack methods specific to each of these very dif- PDF, and then instruments with context monitoring code ferent targets. Additionally, we provide a robust defense the JavaScript within. This combination static and run- method, including a defense against the slightly differ- time approach is tested on a collection of 18623 PDF ent attack proposed in [13] involving the use of existing documents without malware and 7370 with, resulting in characters similar in appearance to normal letters. few false negatives and no false positives [1]. Other research targets attacks not dependent on JavaScript or other parsing vulnerabilities, including one that works to detect these attacks using machine learning on existing 9 Conclusion flagged PDF files using data extracted from the structure of the file as well as its content [3]. One may expect In this paper, we have presented a new class of content this strategy to suffer from the same difficulties experi- masking attacks against the Adobe PDF standard. Af- enced by signature-based antivirus products, namely an ter creating algorithms for each of three content mask- inability to detect malware not already discovered by re- ing attack variants, we perform a comprehensive evalu- searchers. Another work allows PDF documents to be ation showing that each lives up to its theory and oper- opened in an emulated environment to track how they ates in present state-of-the-art systems. Our first attack behave before doing so in the host environment [4]. allows academic paper writers and reviewers to collude Some works slightly closer to ours examine the pos- via subverting the automatic reviewer assignment sys- sibility of causing PDF documents to be rendered differ- tems in current use by academic conferences including ently on different computers, showing how to restrict the INFOCOM, which we simulated. This requires no visi- syntax of the PDF standard to prevent this from occur- ble changes to the paper being reviewed and the addition ring [33] [34]. This attack against data consistency has of just 3-5 custom masking fonts for almost all of the 100 some vague similarity to the concept of content mask- papers tested, easily lost in any paper’s natural fonts. We ing - displaying different content for the human than the show a second attack that renders ineffective plagiarism machine. However, we provide several real-world exam- detection software, particularly Turnitin, to the point of ples of how our content masking attack can subvert real being able to target specific small plagiarism similarity systems, while the impact of the attack in this work is rel- scores to appear natural and evade detection. In our fi- atively limited to the document looking different to hu- nal attack, we successfully place masked content into the mans using different computers. Some works [35] [36] indexes for Bing, Yahoo!, and DuckDuckGo which ren- [37] examine poisoning search results, but this is from ders as information entirely different from the keywords the perspective of presenting false data to the machine used to locate it. Lastly, we provide and test a robust font through website code or manipulations of the PageRank verification algorithm which is more accurate than full algorithm via botnets, an existing threat vector for which document OCR and requires considerably less computa- defenses have been continually adapting. tion power. USENIX Association 26th USENIX Security Symposium 845 References [13] J. Heather, “Turnitoff: Identifying and Fixing a Hole in Current Plagiarism Detection Software,” [1] D. Liu, H. Wang, and A. Stavrou, “Detecting Mali- Assessment & Evaluation in Higher Education, cious Javascript in PDF through Document Instru- vol. 35, no. 6, pp. 647–660, 2010. mentation,” in 2014 44th Annual IEEE/IFIP Inter- national Conference on Dependable Systems and [14] S. T. Dumais, G. W. Furnas, T. K. Landauer, Networks, pp. 100–111, June 2014. S. Deerwester, and R. Harshman, “Using Latent Semantic Analysis to Improve Access to Textual [2] J. Magazinius, B. K. Rios, and A. Sabelfeld, “Poly- Information,” in Proceedings of the SIGCHI Con- glots: crossing origins by crossing formats,” in Pro- ference on Human Factors in Computing Systems, ceedings of the 2013 ACM SIGSAC conference on CHI ’88, (New York, NY, USA), pp. 281–285, Computer & communications security, pp. 753– ACM, 1988. 764, ACM, 2013. [15] M. D. Blei, A. Y. Ng, and M. I. Jordan, “Latent [3] D. Maiorca, D. Ariu, I. Corona, and G. Giacinto, Dirichet Allocation,” Journal of machine learning “A structural and content-based approach for a pre- research, vol. 3, no. Jan, pp. 993–1022, 2003. cise and robust detection of malicious PDF files,” in 2015 International Conference on Information [16] L. K. Pritchard, M. Stephens, and P. Donnelly, “In- Systems Security and Privacy (ICISSP), pp. 27–36, ference of Population Structure Using Multilocus Feb 2015. Genotype Data,” Genetics, vol. 155, no. 2, pp. 945– 959, 2000. [4] F. Schmitt, J. Gassen, and E. Gerhards-Padilla, [17] “Student Paper Migrations.” https: “PDF Scrutinizer: Detecting JavaScript-based at- //guides.turnitin.com/01_Manuals_and_ tacks in PDF documents,” in Privacy, Security and Guides/Administrator/Administrator_ Trust (PST), 2012 Tenth Annual International Con- User_Guide/22_Student_Paper_Migrations, ference on, pp. 104–111, July 2012. 2016. [5] MITRE Corporation, “CVE - Common [18] “How Search Works: Algorithms.” Vulnerabilities and Exposures (CVE).” https://www.google.com/insidesearch/ https://cve.mitre.org/cgi-bin/cvekey. howsearchworks/algorithms.html, 2016. cgi?keyword=pdf, 2016. [19] S. Brin and L. Page, “Reprint of: The Anatomy of [6] K. Selvaraj and N. F. Gutierrez, The Rise of PDF a Large-Scale Hypertextual Web Search Engine,” Malware. Symantec, Recurity Response, 2010. Computer networks, vol. 56, no. 18, pp. 3825– [7] R. Brandis and L. Steller, Threat Modelling Adobe 3833, 2012. PDF. DSTO Defence Science and Technology Ori- [20] “PDFs in Google Search Results.” https: ganisation, 2012. //webmasters.googleblog.com/2011/09/ [8] Adobe Security, PDF Security Reaches New Lev- pdfs-in-google-search-results.html, els with Adobe Reader XI and Adobe Acrobat XI. 2011. Adobe, 2013. [21] G. Williams, “FontForge.” https://fontforge. github.io/, 2017. [9] B. Li and Y. T. Hou, “The New Automated IEEE INFOCOM Review Assignment System,” IEEE [22] J. Berkenbilt, “QPDF.” http://qpdf. Network, vol. 30, no. 5, pp. 18–24, 2016. sourceforge.net/, 2015. [10] “Submitting a Paper.” https://guides. [23] J. Zhao, “Custom Fonts in Latex.” http://math. turnitin.com/01_Manuals_and_Guides/ stanford.edu/~jyzhao/latexfonts.php, Student/Classic_Student_User_Guide/09_ 2012. Submitting_a_Paper, 2016. [24] E. L. Bird, Steven and E. Klein, Natural Language [11] Y. Shinyama, “PDFMiner.” https://euske. Processing with Python. OReilly Media Incorpo- github.io/pdfminer/, 2013. rated, 2009. [12] Adobe, PDF Reference. Adobe Systems Incorpo- [25] Google, “Custom Search Engine.” https://cse. rated, 2006. google.com/cse/, 2016. 846 26th USENIX Security Symposium USENIX Association [26] Yahoo!, “BOSS Hosted Search.” https://boss. yahoo.com/hosted-web-search, 2016. [27] Microsoft, “Bing Search API.” https: //datamarket.azure.com/dataset/ 5BA839F1-12CE-4CCE-BF57-A49D98D29A44, 2016. [28] R. Smith and Z. Podobny, “Tesseract OCR.” https://github.com/tesseract-ocr, 2017. [29] K. J. Ward and V. Costan, “PDF-Extract.” https: //github.com/CrossRef/pdfextract, 2015. [30] D. Malmgren, “textract.” https://textract. readthedocs.io/en/stable/, 2014. [31] S. Hoffstaetter, J. Bochi, and M. Lee, “pytesser- act.” https://pypi.python.org/pypi/ pytesseract/0.1, 2014. [32] D. Stevens, “PDF, Let Me Count the Ways .” https://blog.didierstevens.com/2008/ 04/29/pdf-let-me-count-the-ways/, 2008. [33] G. Endignoux, O. Levillain, and J. Y. Migeon, “Caradoc: A Pragmatic Approach to PDF Parsing and Validation,” in 2016 IEEE Security and Privacy Workshops (SPW), pp. 126–139, May 2016. [34] J. Wolf, “Omg wtf pdf,” 2010. [35] N. Leontiadis, T. Moore, and N. Christin, “A nearly four-year longitudinal study of search-engine poi- soning,” in Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Se- curity, pp. 930–941, ACM, 2014. [36] D. Y. Wang, S. Savage, and G. M. Voelker, “Juice: A longitudinal study of an seo botnet.,” in NDSS, 2013. [37] K. Du, H. Yang, Z. Li, H. Duan, and K. Zhang, “The ever-changing labyrinth: A large-scale anal- ysis of wildcard dns powered blackhat seo,” in 25th USENIX Security Symposium (USENIX Secu- rity 16), USENIX Association. USENIX Association 26th USENIX Security Symposium 847