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Bounded case studies

Show the gap. State only what it proves.

Each example uses benign content, identifies the receiver, names the relevant transformation, and separates a demonstrated representation difference from broader security claims.

01

The invisible separator

A code point survives while the glyph does not announce it.

Representation

The strings project and pro​ject may render identically. The second contains U+200B ZERO WIDTH SPACE between “pro” and “ject.” A raw-code-point scanner sees the distinction immediately. A keyword matcher, tokenizer, search index, or line-breaking engine may handle it differently.

A: U+0070 U+0072 U+006F U+006A U+0065 U+0063 U+0074
B: U+0070 U+0072 U+006F U+200B U+006A U+0065 U+0063 U+0074

Defensive test: preserve the original, enumerate all code points and Unicode categories, then compare NFC and NFKC copies. U+200B generally remains under those normalization forms, so an explicit format-character policy is still required.

Demonstrates

Visually similar text can have a categorically different logical representation that deterministic software can detect.

Does not demonstrate

That every tokenizer preserves the character, that every model understands a code, or that the anomaly is malicious.

02

The cross-script look-alike

Two glyphs, two code points, one human impression.

Confusable

Compare Latin code with cоde, where the second character is Cyrillic Small Letter O (U+043E), not Latin Small Letter O (U+006F). Depending on the font, the difference can be difficult to notice.

Latin:    c o d e   → U+0063 U+006F U+0064 U+0065
Mixed:    c о d e   → U+0063 U+043E U+0064 U+0065

Defensive test: apply Unicode security confusable skeletons and script-mixing analysis as supporting signals, especially for identifiers, domains, commands, and other security-sensitive tokens. Do not use a simplistic “non-ASCII equals hostile” rule.

Demonstrates

Visual comparison and byte/string equality answer different questions.

Does not demonstrate

That all mixed-script prose is deceptive. Multilingual text and legitimate identifiers require context-aware policy.

03

The DOM/render differential

Structural text can exist without entering the ordinary painted view.

HTML
<article>
  <p>Visible report text.</p>
  <span hidden>MT_SAFE_ACK_7F3A</span>
</article>

textContent can recover the hidden descendant. innerText omits ordinary non-rendered descendants. Screenshot OCR sees only painted pixels. None of the views alone is complete: generated content, canvas, malformed layout, and accessibility semantics create additional cases.

Defensive test: collect DOM text, rendered text, computed visibility evidence, bounding boxes, and screenshot OCR. Flag material differences for policy and provenance review.

Demonstrates

Machine extraction and human rendering can receive different text from the same HTML artifact.

Does not demonstrate

That hidden DOM text is inherently hostile. Interfaces, templates, and accessibility patterns use non-visible state legitimately.

04

The parallel semantic plane

Alt text, ARIA, and metadata can describe—or redirect—machine interpretation.

Structure
<button aria-label="Archive the report">
  <svg aria-hidden="true">…</svg>
</button>

EXIF UserComment: "Benign fixture MT_SAFE_ACK_7F3A"

The accessible name is essential to a screen-reader user and may also be the primary label consumed by a UI agent. EXIF or document properties may be appended by a parser even though a person viewing the image or page never opens a properties panel.

Defensive test: preserve field-level provenance. Treat accessibility descriptions as semantic data with a distinct trust label. Inventory metadata separately and pass only allowlisted fields into high-authority reasoning paths.

Demonstrates

Digital artifacts can expose a machine-readable semantic plane distinct from visible pixels.

Does not demonstrate

That accessibility data should be removed. Blanket stripping can harm people and degrade legitimate interpretation.

05

The tokenizer-sensitive space

A tiny surface difference can change model units immediately.

Tokenizer

A documented GPT-2 tokenizer example maps "Hello world" to token IDs [15496, 995], while " Hello world" maps to [18435, 995]. The leading space changes the first token. Other tokenizers can normalize, prepend, collapse, or segment whitespace differently.

surface A: "Hello world"
surface B: " Hello world"
receiver: exact GPT-2 byte-level BPE vocabulary and rules

Defensive test: retokenize with the deployment tokenizer and plausible alternatives; record model and tokenizer versions; compare behavior after trimming, normalization, and detokenization/retokenization.

Demonstrates

Tokenization is a representation layer that can react strongly to a small human-salience change.

Does not demonstrate

That odd/even token IDs carry meaning, or that a token convention survives another tokenizer without a shared mapping.

06

The ordinary word-choice bit

Natural variation creates bandwidth—and statistical cost.

Linguistic

In a toy shared codebook, calm → 0 and quiet → 1. A sender chooses one contextually acceptable form; a receiver reproduces the candidate set and mapping. This is easy to state but difficult to engineer reliably because synonyms differ in register, collocation, sense, and natural frequency.

cover intent: The room remained ____.
state 0: calm
state 1: quiet

Defensive test: compare lexical-choice distributions with a matched author, genre, and context; score contextual probability; apply controlled paraphrase; test whether a suspected pattern survives.

Demonstrates

A secondary signal can occupy a legitimate linguistic choice and remain unobtrusive to a casual reader.

Does not demonstrate

That the choice is hidden without a codebook, that all synonyms are interchangeable, or that one sample proves a covert channel.

07

Watermark versus payload

Similar carriers, different operational questions.

Signal design
WATERMARKHypothesis test

A keyed generator favors one pseudorandom token subset. The detector aggregates whether the sequence contains more favored tokens than expected and answers a provenance question.

STEGANOGRAPHYMessage recovery

A coding scheme maps an external bitstream to token or semantic choices. The receiver reconstructs an arbitrary payload, often requiring exact state and distribution knowledge.

Defensive test: use the scheme-specific detector when the key is known; otherwise distinguish generic anomaly evidence from proof. Test robustness under edits and paraphrase.

Demonstrates

Machine-oriented linguistic signals can be imperceptible to humans yet statistically strong for an equipped detector.

Does not demonstrate

That an unkeyed general model can infer the signal, or that all machine-generated text carries the same watermark.

08

The inert indirect-injection experiment

Separate parser access from instruction uptake.

Methodology

A controlled test places the same benign marker instruction in a visible field, a non-rendered field, and an encoded field. The trusted user asks for an unrelated summary. The experiment logs each stage: artifact, extracted text, detector decision, model input, and exact marker output.

marker: MT_SAFE_ACK_7F3A
allowed effect: emit that inert string
forbidden: secrets, tools, egress, state change, persistence

Defensive test: include clean negatives, visible positives, matched benign structural controls, explicit decoder-capability controls, and transformation controls. Label a positive result “benign instruction uptake under tested conditions.”

Demonstrates

Whether a specific pipeline extracted and followed a harmless external instruction under recorded conditions.

Does not demonstrate

Comprehensive vulnerability, malicious impact, model-wide behavior, or security across versions and preprocessing stacks.

Make the examples interactive

Inspect code points, DOM views, metadata fields, and toy linguistic signals.

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