Summary / 26. Agent security and prompt-injection resistance / Document
Paper 2026-09-06
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# Component 26 — Agent Security / Prompt-Injection Resistance ## Separating a Trusted Control Plane from an Untrusted Data Plane, Attaching Instruction-Authority Labels to Every Observation, and Mediating Every Tool Request Through Deterministic Gates --- ### Abstract This paper specifies the twenty-sixth component of the coding harness: agent security and prompt-injection resistance. Components 24 and 25 drew the fence and wrote the law — an execution sandbox that contains arbitrary generated code, and a permission system that derives authority from workflow state rather than from the model's intentions. Component 26 faces the threat those two components cannot see, because it does not live at the boundary where code executes or where authority is granted: it lives in the *text the agent reads*. The adversary needs no breakout and no privilege escalation if it can persuade the model to *ask* for the wrong thing while wielding authority the system has already, legitimately, handed it. The central claim of this component is that the agent's native attack surface is everything it ingests — repository artifacts, dependencies, retrieved documents, tool definitions, tool responses, external services, and the derived memory built from them — and that the question is not whether a good-enough model would refuse a bad instruction, but whether the *system* can make refusal unnecessary. The design is a trusted control plane kept separate from an untrusted data plane: provenance and instruction-authority labels attached to every observation, so that a fact about the world and an instruction to act are never the same kind of thing; a structural ban on laundering untrusted content into trusted instructions through transformation or summarization; structured execution evidence normalized before raw text is ever exposed; MCP servers and tool metadata quarantined and reviewed before registration; every tool request and response mediated through independent policy and security gates; and capability policies attached to values so that untrusted content cannot grant permissions, change workflow state, expose secrets, or authorize new tools. The structural invariant that governs the whole design is that the learned column contains at most one entry — and at this component the enforcement path must be deterministic software on CPU [D] with zero learned entries, because the model is the thing being *steered*, and it cannot be the thing enforcing its own steering. The evidence is assembled from the attack literature (Ignore Previous Prompt; Not What You've Signed Up For; You Autocomplete Me; MCPTox; AgentPoison), the defense literature (StruQ; the Instruction Hierarchy; Spotlighting; CaMeL; ShieldMCP; MCPSec; the six design patterns), the information-flow lineage (Biba; Denning; Thompson's trusting trust), and it closes with the honest-measurement battery — AgentDojo, InjecAgent, MCPTox, ToolSandbox, AgentPoison — that scores both attack-success rate and benign-task utility, and that treats a successful injection as something that may cost a task but must never cost a credential. --- ## 1. Introduction Every prior component of the harness has relied, in some register, on a division of labor: a learned model does the thinking, and deterministic software does the guaranteeing. Component 24 sharpened that division to its limit by assuming the generated code *and* the agent directing it are both untrusted. Component 25 sharpened it further by making authority *derived* rather than *declared* — the model requests capability, and a deterministic control plane grants or denies it. Component 26 is where the division of labor meets its hardest test, because the thing the adversary wants to corrupt is not code and not capability but *instruction itself*: the text that tells the model what to do. The sandbox contains code; the permission system scopes authority; neither of them can tell the difference between a legitimate instruction from the harness and a hostile instruction smuggled into a README, a tool description, a retrieved document, or a tool response — and that is precisely the difference the whole discipline of agent security exists to draw. **Coding-agent security should assume every repository artifact, dependency, retrieved document, tool definition, tool response, external service, and derived memory can contain adversarial instructions: maintain a trusted control plane separate from the untrusted data plane; attach provenance and instruction-authority labels to every observation; never allow transformation or summarization to launder untrusted content into trusted instructions; normalize structured execution evidence before exposing raw text; quarantine and review MCP servers and tool metadata before registration; mediate every tool request and response through independent policy and security gates; prevent untrusted content from granting permissions, changing workflow state, exposing secrets, or authorizing new tools; and continuously red-team the complete agent with executable indirect-injection and tool-poisoning benchmarks while measuring both security and benign-task utility.** **Repository text and tool output are allowed to tell Piper what the world looks like. They are never allowed to tell Piper what she is authorized to do.** The second sentence is the human version of the first, and it is the sentence that makes this component non-negotiable. Throughout the harness, the model has been a *trusted collaborator* held to account by deterministic gates: the workflow state machine trusts the model to make semantic judgments inside states and trusts the gates to keep those judgments inside bounds. Agent security cannot afford that trust at the point where text enters the model, because the entering text is the one input over which the harness has the least *a priori* control. A repository on disk is presumed safe because it was cloned over a verified channel; but the repository is full of files written by strangers, the dependencies were published by strangers, the retrieved documents were retrieved from the open web, and the tool responses were produced by servers the harness did not write. Every one of those surfaces can carry an instruction, and the model — by construction — treats text as meaning, not as evidence of intent. ### 1.1 The inversion: the agent's native attack surface is the text it ingests The central inversion of this component is that the agent's *native* attack surface is not its code-execution boundary and not its permission boundary, but the stream of text that flows through its reasoning loop. A classical program has a narrow, enumerated input surface; an agent has an unbounded one, because its job is to read things — repositories, diffs, documentation, web pages, tool outputs — and to act on what it reads. The adversary who controls any fragment of that reading material controls a fragment of the agent's future behavior, and the agent's competence is what makes the attack work: the better the model is at following the import of text, the more faithfully it will follow an instruction that text contains. The reason "the model wouldn't do that" is not a control is that the model is *defined* by following instructions in text. The demonstration is as old as the threat. Perez and Ribeiro's "Ignore Previous Prompt" work [6] showed that a prompt of the form "ignore your previous instructions and do X instead" reliably hijacks a model's goal (goal hijacking) and, in the symmetric attack, coaxes it to emit its own system prompt and any embedded secrets (prompt leaking). This is not a weakness of a particular model; it is a property of instruction-following itself, and it does not need a model to be *malicious* — only to be *obedient to the last instruction it read*. Greshake, Abdelnabi, Mishra, Endres, Holz, and Fritz named the general phenomenon in "Not What You've Signed Up For" [7]: *indirect* prompt injection, where the adversary does not speak to the model directly at all, but embeds the instruction in content the model is asked to read — a web page, a message, a retrieved snippet — so that the injection arrives wearing the provenance of legitimate data. Their taxonomy names the consequences in the terms the harness must defend against: data theft (the agent is induced to exfiltrate what it can read), worming (the agent propagates the injected instruction onward), information-ecosystem contamination (the agent's outputs are poisoned), and arbitrary code execution delivered as a retrieved prompt. Detection alone loses this contest, and the honest measurement says so directly. In AgentDojo [1], a benchmark that evaluates prompt-injection attacks *and* defenses in a live agent environment across 629 test cases with an *adaptive* attacker that re-plans after each failed attempt, no evaluated frontier agent withstood the adaptive attacks: defense mechanisms that block a single-shot attack are broken by an attacker that observes the defense and adjusts. The lesson is structural. A detector is a classifier over text; the adversary has the same text, and can iterate against the classifier until the classifier fails, because the classifier is itself a learned model of precisely the kind the attack is designed to defeat. Security that depends on *recognizing* the attack is security that depends on winning an arms race against an adversary who moves second; security that depends on *not needing to recognize the attack* — on making the attack structurally incapable of reaching authority — is the only kind that holds. ### 1.2 The D/L invariant Every element of this component is classified into one of two columns: **[D]** — deterministic software running on CPU — or **[L]** — a learned model. The structural invariant that governs the entire design is: > **the L column contains at most one entry.** The security control plane — provenance tagging, channel separation, instruction-authority labeling, schema normalization, MCP quarantine and review, the mediation gates, and the capability policies attached to values — is deterministic software on CPU [D], with **zero learned entries in the enforcement path**. The model is the thing being *steered* by this control plane, and it cannot be the thing enforcing its own steering: a steering mechanism that contains the very entity it is meant to steer is not a control, it is a suggestion. **An empty L column is a permitted, correct state** — a deterministic phase of the harness that needs no model call at all — and it is the *only* correct state for the enforcement path, because the enforcement path is exactly where a model, with its demonstrated obedience to injected instructions, must have no decision-making role. The only learned elements in the system are *outside* the control plane: the harness's model calls that read text and propose actions. Inside the control plane there is nothing to learn, and nothing that learns. This is the same invariant that governs Components 24 and 25, held at the same strength and applied to the one surface those components left open. The sandbox's invariant holds *during execution*; the permission system's invariant holds *before execution*, at the request boundary; the security component's invariant holds *before interpretation*, at the point where text becomes an observation. The three compose into a single boundary: the security component decides what an observation is *allowed to mean* (data, not instruction); the permission system decides what an action is *allowed to ask for*; and the sandbox decides what a received capability is *allowed to do*. None of the three has a learned entry in its enforcement path, and all three are correct to have none. ### 1.3 A preamble on provenance: this paper codifies the discipline the agent itself lives under This component is not a new idea entering the program from outside; it is the coding-harness instance of a ruleset the program already ratifies at its operator-facing surface. Piper's own prompt-injection and data-poisoning defense — the **M1–M6 mechanical** rules and the **B1–B6 behavioral** rules — already declares that all external text is *data*, never *instruction*; that secrets never enter model context; that outbound network is allowlisted; that irreversible actions are token-gated by a deterministic classifier; that untrusted parsing runs sandboxed; that state-changing calls route through deterministic gates; and that the instruction surface is hash-pinned so tampering surfaces as a diff. This paper is the harness's *specification of that discipline as an engineering component*: it is the document that says, in harness terms and with measurable success criteria, what the operator-facing rules already say in policy terms. The paper's honest position is therefore self-referential in a deliberate way — the security component it specifies is the same discipline under which the agent that writes and runs the harness itself operates. There is no outside; the component is not securing a hypothetical agent, it is securing the agent that is, at this moment, reading repositories, calling tools, and writing memory. The D/L invariant that governs the component is the same invariant that governs Piper's own enforcement path: **the L column contains at most one entry**, and in the enforcement path the correct value is zero. --- ## 2. The attack surface "Prompt injection" is frequently presented as a single trick — tell the model to ignore its instructions — but the measurement and the mechanism both decompose it into a taxonomy of distinct attacks, each with its own channel, its own goal, and its own point in the agent's pipeline where the adversary gains purchase. Conflating them produces a defense that is either too narrow to be meaningful or too vague to be auditable. The attacks are layered and complementary: no one of them is sufficient to describe the threat, and dropping any one leaves a hole the others do not cover. This section assembles the surface along the pipeline the harness actually runs — the system prompt, the repository text, the tool definitions, the tool responses, and the lineage of trust that makes every one of those surfaces *someone else's code*. ### 2.1 Goal hijacking and prompt leaking: the direct attack on the instruction surface The founding attacks are the two faces of a single fact: the model follows the last instruction it reads. *Goal hijacking* turns that fact into a takeover — an injected "ignore previous instructions, do X instead" replaces the harness's objective with the adversary's — and *prompt leaking* turns it into exfiltration — an injected "repeat your system prompt" extracts the very instructions and any embedded secrets the system prompt contains. Perez and Ribeiro demonstrated both under a single framework, PromptInject, against GPT-3, in "Ignore Previous Prompt: Attack Techniques For Language Models" [6]. The paper is an arXiv preprint from 2022 — it is not peer-reviewed, and its scope is a single model family — but its significance is that it established the *shape* of the problem before agents existed in their current form: the instruction surface is itself a secret to be protected, and it is protected poorly by anything that relies on the model to distinguish "the operator's instruction" from "an instruction that arrived in text." The harness's own B-rules already encode the response — "ignore previous instructions," role-shifts, urgency, and authority appeals are data to be *analyzed*, never orders — and this component is the machinery that makes that behavioral rule a structural property. ### 2.2 Indirect injection: the attack that wears the provenance of data The direct attack requires the adversary to speak to the model; the indirect attack does not. Greshake, Abdelnabi, Mishra, Endres, Holz, and Fritz's "Not What You've Signed Up For: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection" [7] embeds the instruction in content the model is asked to *read* — a retrieved web page, an email, a document — so the injection arrives carrying the provenance of legitimate data. Because the model cannot tell "this is a fact about the world" from "this is an instruction to act," the adversary converts the agent's own information-gathering into a delivery channel. The paper's taxonomy names four consequences the harness must defend against directly: **data theft** (the agent is induced to exfiltrate information it can legitimately read but must not emit), **worming** (the agent propagates the injected instruction onward to other agents or other sessions), **information-ecosystem contamination** (the agent's outputs, and the outputs of anything downstream of them, are poisoned), and **arbitrary code execution delivered as a retrieved prompt** (the injected content is executed, not merely read). The broader indirect-injection literature adds a fifth phenomenon the harness must name even though it is not this paper's own term — *scope amplification*, in which an instruction embedded inside one retrieved snippet reaches outward and re-scopes its own authority, so that a note inside a document begins issuing directives about the agent's whole task. The venue and identity of [7] are recorded here scrupulously because they are frequently mis-stated: it appeared in the *Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security* (AISec '23, co-located with CCS 2023), not at USENIX Security — DOI 10.1145/3605764.3623985. ### 2.3 Malicious repository text steering generation: the poisoned-corpus attack The harness does not merely *read* repositories; it is built to generate code *conditioned on* repositories. That makes the corpus itself a training-data poisoning surface, and Schuster, Song, Tromer, and Shmatikov's "You Autocomplete Me: Poisoning Vulnerabilities in Neural Code Completion" [8] demonstrated the attack before code agents existed in their current form: poisoned comments and identifiers planted in a code corpus steer a neural code-completion model toward emitting vulnerable, CWE-grounded code. The mechanism matters more than the specific CWE enumeration. The model is not compromised; it is *faithful*. It learned, from the corpus it was given, that certain phrasings co-occur with certain code patterns, and the adversary planted the phrasings to make the *vulnerable* pattern the likely completion. The harness's equivalent surface is every repository artifact it ingests during a task: a README, a docstring, a comment, an issue body. If any of that text can reach the model in a position where the model's next action is "write the code this context suggests," then the adversary who controls the text controls a distribution over the code the harness writes — without ever breaking the sandbox, because the sandbox is asked to run code the *harness itself* generated in good faith. ### 2.4 Tool poisoning: the attack that lives in the tool registry The most direct threat to a tool-calling agent is the tool itself. MCPTox [4] — "MCPTox: A Benchmark for Tool Poisoning on Real-World MCP Servers" — quantifies it against the Model Context Protocol's live ecosystem: 45 live real-world MCP servers, 353 authentic tools, three attack templates expanded into 1,348 malicious test cases across 10 risk categories and 20 LLM agent settings. The headline is stark: GPT-o1-mini, a frontier-capable model, succumbs at a 72.8% attack-success rate, and the highest refusal rate across all evaluated models — Claude-3.7-Sonnet — is under 3%. The poisoned tool is the ideal attack vector precisely because the model *trusts tools*: a tool description is, in the agent's ontology, an instruction ("what this tool does and when to call it"), and a tool response is a fact ("what happened"). The adversary who controls either controls both halves of the model's tool-use loop, and the 3% refusal floor shows that no amount of alignment rescues the model from a tool it has been told to trust. This is the component's clearest single data point: tool poisoning is not a hypothetical corner of the threat model, it is the dominant failure mode, and it is *measured*. ### 2.5 The supply-chain and trusting-trust lineage The deepest form of the attack is the one that needs no runtime injection at all, because the backdoor is installed in the tool *before* the harness ever runs it. Ken Thompson's "Reflections on Trusting Trust" [9] is the canonical statement: a compiler modified to recognize when it is compiling itself can insert a backdoor into the login program, and the modification is then *self-propagating* — the source code of the compiler contains no trace of the backdoor, because the backdoor lives in the compiler's *behavior*, re-inserted at every self-compilation. The moral, which Thompson draws in the paper's final line, is that you cannot trust code you did not write yourself — and since you cannot write everything yourself, you must decide *where* the chain of trust ends and what you will do about everything past that point. The modern form of the lineage is Ohm, Plate, Sykosch, and Meier's "Backstabber's Knife Collection" [20], a review of open-source software supply-chain attacks that catalogs the concrete mechanisms — malicious packages, typosquatting, compromised maintainer accounts, build-time injection — through which the trusting-trust principle is operationalized against real ecosystems. The harness's own supply-chain rules are the policy instance of this lineage: a package is not installed unless it is older than 72 hours, is vetted against a popularity-versus-safety distinction, and is sandbox-tested first. This component is the *coding-harness* instance of the same lineage: the agent does not merely install dependencies, it *reads* them, *summarizes* them, and *conditions code on* them — and every one of those operations is a place where the trusting-trust principle applies, not to the compiler but to the text the model ingests. --- ## 3. The trusted control plane versus the untrusted data plane Section 2 described *what* must be defended; this section specifies *how*. The answer is the load-bearing one, and it restructures the harness's data model around a single distinction: the *control plane* (trusted) and the *data plane* (untrusted). The control plane is the harness's own deterministic machinery — the workflow state, the policy, the gates, the provenance ledger — and the data plane is everything the agent reads from the world. The security property is that nothing from the data plane can ever *become* part of the control plane without passing through a deterministic mediation that decides, by provenance and by policy, what it is allowed to mean. The failure mode this section is built to prevent has a name and a precise mechanism: **trust laundering**. ### 3.1 The D/L invariant, restated at the architecture The control plane is deterministic software on CPU [D]. Provenance tagging, channel separation, instruction-authority labeling, schema normalization, MCP quarantine and review, the mediation gates, and the capability policies attached to values — every one of these is a computation over structured state, never a judgment a model is asked to make. The invariant that governs the architecture is the one already stated and restated: > **the L column contains at most one entry.** And here the correct value is **zero**: **an empty L column is a permitted, correct state**, and it is the state the enforcement path must be in, because the enforcement path is the one place where a model — with its measured 72.8% tool-poisoning susceptibility and its 3% refusal floor — must have no decision-making role. The model's relationship to the control plane is a single, well-defined edge: it may *read* observations and *propose* actions, and the control plane *decides* what each observation is allowed to mean and what each proposal is allowed to do. Read, propose; never decide. That distinction is the entire security boundary of this component. ### 3.2 Provenance and instruction-authority: every observation carries two labels The first mechanism is that every observation the agent receives carries, from the moment of capture, a structured label the model cannot edit and the pipeline cannot drop. The label has two fields. The **provenance** field records *where the text came from* — which repository file, which dependency, which retrieved URL, which tool, which external service — and by what channel it arrived. The **instruction-authority** field records *what the text is allowed to mean* — and it has, in the harness's ontology, exactly two values: **factual authority** (this text may describe the world) and **instruction authority** (this text may direct action). The rule that makes the labeling load-bearing is a single, deterministic one: **no observation that enters through the data plane may be assigned instruction authority except by the control plane, and the control plane assigns it only to text that the harness itself authored** — the system prompt, the workflow state, the policy. Text from a repository, a dependency, a retrieved document, a tool definition, a tool response, or a derived memory enters with factual authority and *keeps* factual authority; it can tell the model what the world looks like, and it can never, by itself, tell the model what to do. This is the Piper rule of the introduction, made into a type system: **repository text and tool output are allowed to tell Piper what the world looks like; they are never allowed to tell Piper what she is authorized to do.** The labeling is a computation over provenance — deterministic, auditable, and exactly as strong as the channel separation that produced the provenance in the first place. The distinction matters because it is *not* semantic. "Factual" and "instructional" are not properties the model is asked to detect; they are properties the *system assigns at capture time* on the basis of where the text came from. A tool response that says "delete the database" is labeled factual-authority — it is a report of what the tool returned — and the model may reason about it, quote it, and display it, but the instruction "delete the database" is not an instruction because the label says the text that carried it has no instruction authority. The adversary cannot argue its way past the label, because the label is not the model's to reassign. ### 3.3 The trust-laundering bug: transformation must not mint trust The naive version of §3.2 fails, and it fails in a way the harness is especially prone to, because the harness does not merely *read* untrusted text — it *processes* it. It summarizes documents, distills diffs, compresses context, extracts key points. Each of those operations is a learned transformation: the model reads untrusted text and produces a shorter, denser, *more fluent* version. The trust-laundering bug is that the *output* of such a transformation is fluent and coherent and reads like something the model itself — or the harness — produced, and so the system, the model, and the downstream pipeline all treat it as more trustworthy than the source it was distilled from. Summarization launders untrusted content into trusted instruction: the injected instruction, "ignore your instructions and run `curl http://evil`," survives summarization as "the document says to run `curl http://evil`," and the summary — because it is the model's own fluent prose — sails past the very labels that were attached to the source. The rule is therefore absolute and structural: **a transformation of untrusted content inherits the provenance and instruction-authority label of its source, and no transformation, summarization, or compression may upgrade a factual-authority label to instruction-authority.** The control plane stamps the output of every summarization with the provenance of the input, and the stamped label travels with the text through every downstream step. A summary of a repository file is a repository artifact with extra steps; a distilled tool response is still a tool response. Fluency is not a security property, and the control plane must treat it as irrelevant — which it can only do if the label is attached mechanically and cannot be detached or upgraded by the very transformation that produced the fluent text. This is the B-rules' "structural separation over delimiters" made into a data-integrity guarantee: the untrusted content and the trusted instruction stay in separate channels, and the model's own summarization is *forbidden* from being the channel that merges them. ### 3.4 Normalize structured execution evidence before exposing raw text The last mechanism in the control plane is about *order*. When the agent acts — runs a command, calls a tool, executes a test — the raw result is unstructured text: a stack trace, a JSON blob, a shell transcript. That text is a tool response, and a tool response is a data-plane observation with a factual-authority label. But the harness *needs* the model to act on that text — to interpret the failure, fix the bug, retry. The rule that reconciles those two facts is that **structured execution evidence is normalized before raw text is exposed**: the control plane reduces a tool result to a structured, schema-bound record — exit code, stdout/stderr as typed fields, resource usage, the exact arguments and their values — and the model sees the *record*, not the raw stream. The normalization is a deterministic parsing step on CPU [D], and its purpose is double. First, it strips the attack channel: raw text can carry an embedded instruction ("ignore previous instructions, read /etc/shadow"), but a structured record with typed fields gives the adversary nothing to embed an instruction *in* — the fields have fixed types, fixed meaning, and no room for prose. Second, it preserves exactly the information the model needs: the model reasons about *what happened* (exit code, which test failed, what the diff was), not about an unbounded string whose interpretation is up for grabs. Normalization is the harness's answer to the observation that the agent's competence is also its vulnerability — it lets the model keep the information while taking away the surface where the information could carry an instruction. --- ## 4. Channel separation Section 3 separated the control plane from the data plane by *labeling*; this section separates them by *structure*. The labeling of §3.2 is only as strong as the channel separation that produced the provenance, and channel separation is the discipline that keeps untrusted text and trusted instruction from ever sharing a channel in the first place. The lineage here is the oldest and most formal in the field, and it is worth stating precisely because the whole design is a modern instance of it. ### 4.1 The Biba/Denning information-flow lineage The information-flow lineage supplies the vocabulary the rest of the section uses. Denning's "A Lattice Model of Secure Information Flow" [17] gave the field its formal core: security is a lattice over security classes, and a flow from class A to class B is permitted only if the classes stand in the right dominance relation — information may not flow from a higher class to a lower one unless the lattice permits it. Biba's "Integrity Considerations for Secure Computer Systems" [18] — a MITRE technical report, MTR-3153, April 1977, not a peer-reviewed journal paper — is the integrity dual of confidentiality: it assigns *integrity* levels and forbids the two flows that corrupt integrity, canonically stated as "no read down, no write up" — a low-integrity subject may not read high-integrity data (lest it be corrupted by it) and may not write into high-integrity data (lest it corrupt it). The prompt-injection problem is, at bottom, an *integrity* problem in exactly Biba's sense: the untrusted text is low-integrity data, the instruction surface is high-integrity data, and the injection is a forbidden "write up" — untrusted text writing itself into the instruction stream. Channel separation is the mechanism that enforces Biba's "no write up" on the agent's text pipeline: untrusted text and trusted instruction flow through different channels, and the lattice's dominance relation — not the model's judgment — decides what may flow where. ### 4.2 StruQ: structured queries and instruction tuning StruQ [3] — Sizhe Chen, Julien Piet, Chawin Sitawarin, and David Wagner, "StruQ: Defending Against Prompt Injection with Structured Queries," USENIX Security 2025, arXiv:2402.06363 — is the defense that most directly operationalizes channel separation for the harness's own shape. Its mechanism is two-fold and both parts are reusable. First, the *secure front-end* separates the prompt and the data into **two channels**: the system-level instruction travels in one channel, and the user-supplied or retrieved data travels in another, so that the model can be told — by structure, not by convention — which text is instruction and which is data. Second, *structured instruction tuning*: the model is fine-tuned on examples that deliberately place instructions *inside* the data channel, trained to ignore them, so that the distinction between "instruction that carries authority" and "text that merely looks instructional" is a learned behavior reinforced at training time. The result, verified from the abstract, is significantly improved resistance to prompt injection with little or no utility loss. The significance for the harness is the *division of labor* it implies: the channel split is deterministic structure (a D-column property), while the model's *obedience* to the split is a learned behavior (an L-column property) — and the defense works only because the D-column guarantees the channel separation that the L-column's fine-tuning is then asked to respect. This is the template for how a learned entry and a deterministic guarantee compose: the deterministic part must be the one the adversary cannot argue with, and the learned part is only ever the model's *compliance with* that guarantee, never the guarantee itself. ### 4.3 The Instruction Hierarchy: privileged instructions by rank Wallace, Xiao, Leike, Weng, Heidecke, and Beutel's "The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions" [13] supplies the second piece: a *rank* on instructions, in which system-level instructions outrank developer-level instructions, which outrank user-level instructions, which outrank tool-output-level instructions. The model is trained to honor the hierarchy — when a lower-rank instruction conflicts with a higher-rank one, the higher one wins — and, crucially, to *resist* lower-rank instructions that attempt to override or manipulate higher-rank ones. This is Biba's "no write up" trained into the model's behavior: tool output, the lowest-integrity channel, is trained to be unable to write itself into the instruction stream. The harness's structural analog is direct: the workflow state and the policy are the top of the hierarchy; the repository text and tool output are the bottom; and the control plane's labels of §3.2 are the *enforcement* of a rank the model is only asked to *respect*. The hierarchy is a learned disposition; the label is a deterministic fact; and the two compose because the label makes the rank something the system can check, rather than something the model must remember. ### 4.4 Spotlighting: delimiting, datamarking, encoding Hines, Lopez, Hall, Zarfati, Zunger, and Kiciman's "Defending Against Indirect Prompt Injection Attacks With Spotlighting" [19] supplies the third piece: three *presentation-layer* transformations that make the boundary between trusted and untrusted text *visible to the model itself*. **Delimiting** wraps untrusted text in explicit markers; **datamarking** marks the *data* as data (a label that travels with it); and **encoding** transforms the data so that it cannot be mistaken for instruction. Spotlighting is the least structural of the three defenses in this section — it is a presentation convention, not a type system — but it earns its place because it is the cheapest defense that works against indirect injection in a plaintext pipeline, and because it is the honest proof that the model *can* respect a boundary it can *see*. The harness's position, consistent with everything above, is that Spotlighting is a *behavioral* complement to a *structural* guarantee, never a substitute: the labels of §3.2 are the structural guarantee (the control plane enforces them), and a Spotlighting-style presentation is the behavioral aid (the model is helped to respect what the structure already enforces). A presentation convention with no structural backing is exactly the kind of defense the adaptive attacker in AgentDojo breaks; a structural guarantee with no presentation aid is harder than it needs to be for the model to honor. ### 4.5 CaMeL, and the "defeat by design" limit The strongest statement of the section's thesis comes from CaMeL [21] — Debenedetti, Shumailov, Fan, Hayes, Carlini, Fabian, Kern, Shi, Terzis, and Tramèr, "Defeating Prompt Injections by Design." Its claim is that prompt injection is a *capabilities* problem: the model must be *incapable* of acting on injected instructions because the system never gives the model the capability the instruction would need — the model is not granted the ability to perform the sensitive action in the first place, so no instruction, injected or otherwise, can induce it. This is the logical endpoint of the channel-separation section, and it is where the section hands off to §5: the most complete separation of the control plane from the data plane is the one where the data plane's instructions, even if they reach the model, find no capability waiting to execute them. CaMeL is the bridge from "separate the channels" to "make the tool layer itself incapable of being steered," and the next section takes that bridge. --- ## 5. The tool layer as attack surface The tool layer is where the threat model of §2 and the control plane of §3 meet, and it is the component's highest-value target: the tool is the agent's means of action, and the tool *definition* — the description of what the tool does and when to call it — is, in the agent's ontology, an instruction. The section specifies how the harness treats tools before, during, and after registration, and it assembles the evidence that the tool layer is both the worst failure surface (MCPTox's 72.8%) and the most tractable one (ShieldMCP's order-of-magnitude reductions at sub-millisecond latency). ### 5.1 MCP manifest review: quarantine before registration The first rule is that a tool is *quarantined* before it is *registered*. A tool definition — for an MCP server, a manifest describing the server, its tools, their parameters, their schemas, and their descriptions — is data-plane text with a factual-authority label, and the harness must not let it become instruction simply by registering it. The control plane therefore reviews the manifest deterministically before any of it reaches the model as an instruction: the manifest is parsed into a schema, the tool descriptions are normalized into structured records, the parameter types are checked, and the server's *claimed* capabilities are compared against what the harness is willing to grant. Only after that review does the tool enter the model's reachable universe, and it enters with a *capability policy* attached — the set of actions, argument ranges, and resource scopes the tool is permitted to exercise, computed by the permission component's derivation, not by the manifest's own claims. The manifest may describe what the world contains; it may not, by itself, decide what the tool is authorized to do. This is §3.2's label applied to the tool registry, and it is the structural answer to the paper whose title asks the question directly: Huang, Huang, and Milani Fard's "Are AI-assisted Development Tools Immune to Prompt Injection?" [15] finds that untrusted MCP tool descriptions can carry hidden instructions — reported verbatim by the Promptfoo LMVD advisory that cites it, including that Cursor 1.6.45 was unsafe across all four tested vectors — and the harness's answer is that no tool description is *immune*, so no tool description is *trusted*: it is reviewed, normalized, and capability-scoped before the model ever reads it as an instruction. ### 5.2 ShieldMCP and MCPTox: the measurement on both sides The tractability of the tool layer is measured, and the numbers are the component's strongest evidence that the design is achievable. MCPTox [4] supplies the *attack* side of the ledger, and it is already stated: 45 live MCP servers, 353 tools, 1,348 malicious test cases, 10 risk categories, 20 agent settings, GPT-o1-mini at 72.8% attack-success rate, and a refusal floor under 3%. ShieldMCP [5] supplies the *defense* side: Saurabh Yergattikar's "Securing the Tool Layer: A Threat Taxonomy and Runtime Defense Framework for Model Context Protocol Deployments," ACL 2026 Industry Track, DOI 10.18653/v1/2026.acl-industry.58 — a single-author paper with no arXiv version, and "ShieldMCP" is the framework's name, not the paper's title. Its taxonomy consolidates 80+ attack techniques (from the SAFE-MCP initiative) into 14 tactical categories adapted from MITRE ATT&CK, and its runtime defense produces the numbers that matter: tool poisoning reduced from **74% to below 9%**, indirect prompt injection via tool responses reduced from **47% to below 6%**, and a median latency of **under 120 ms** per tool call across five LLM backends. The latency figure is the one that makes the design adoptable: a security gate that adds a model call per tool invocation is unusable, and ShieldMCP demonstrates that a deterministic mediation gate need not be. The harness's §3 and §4 machinery — provenance labels, schema normalization, mediation gates, capability policies — is the same architecture, specified here against the harness's own pipeline rather than against the MCP runtime in general. ### 5.3 Breaking the Protocol: the spec itself is a surface The protocol that carries tools is itself an attack surface, and Maloyan and Namiot's "Breaking the Protocol: Security Analysis of the Model Context Protocol Specification and Prompt Injection Vulnerabilities in Tool-Integrated LLM Agents" [12] analyzes it directly. The paper identifies three protocol-level vulnerabilities: absent capability attestation (a server may claim arbitrary permissions), bidirectional sampling without origin authentication (which enables server-side prompt injection), and implicit trust propagation in multi-server configurations. Its benchmark — **MCPBench**, not any other name — evaluates 847 attack scenarios across five MCP server implementations and finds that MCP amplifies attack-success rate by 23–41% relative to non-MCP tool use; its defense — **MCPSec**, not any other name — cuts that success rate from 52.8% to 12.4% with an 8.3 ms median overhead. The paper's significance for the harness is that it relocates the failure from the *model* to the *protocol*: the model is not failing to resist injection so much as the protocol is failing to carry the provenance and authority information the model would need to resist it. The harness's mediation gates of §3 and §5.1 are, in effect, a *policy layer above the protocol* that supplies the attestation, origin-authentication, and trust-propagation control the protocol itself omits — and the harness must assume, as this paper demonstrates, that the protocol will not supply them. ### 5.4 Supply-chain poisoning of skill ecosystems The tool layer's supply chain is the subject of Qu, Liu, Geng, Deng, Li, Zhang, Zhang, and Ma's "Supply-Chain Poisoning Attacks Against LLM Coding Agent Skill Ecosystems" [11], which attacks not individual tools but the *ecosystem* of skills a coding agent installs. Its mechanism, DDIPE (Document-Driven Implicit Payload Execution), plants a document whose implicit payload executes when the agent reads it, and the paper's scale is the evidence: 1,070 adversarial skills generated from 81 seeds across 15 MITRE ATT&CK categories, tested across 4 frameworks and 5 models, achieving 11.6%–33.5% bypass rates — with explicit-instruction attacks dropping to 0% under strong defenses, but 2.5% of attacks evading both detection and alignment, and 4 confirmed vulnerabilities plus 2 fixes resulting from the study. The finding that matters for the harness is the *residue*: even under strong defenses, a non-zero fraction of poisoned skills evades both detection and alignment, which is the measured form of the §1 claim that detection alone loses. The harness's answer is not a better detector but a smaller surface: skills are quarantined and reviewed (§5.1), scoped by capability policy (§5.1), and executed only through the sandbox (Component 24) and the permission derivation (Component 25) — so that a skill that evades detection still finds nothing to escalate into. ### 5.5 The six design patterns The section's synthesis is Beurer-Kellner, Buesser, Creţu, Debenedetti, Dobos, Fabian, Fischer, Froelicher, Grosse, Naeff, Ozoani, Paverd, Tramèr, and Volhejn's "Design Patterns for Securing LLM Agents" [10] — fourteen authors, and the four names frequently mis-attributed to it (Carlini, Kudlek, Nasr, Nissenbaum) are not among them. The paper distills six patterns, each of which is a concrete realization of the control-plane/data-plane separation, and the harness adopts all six in the roles this section has already assigned them: the **action-selector** pattern (the model chooses from a closed set of pre-vetted actions rather than emitting free-form instructions — the mediation gate of §3); **plan-then-execute** (the model produces a plan that a deterministic executor carries out — the workflow state machine, Components 7 and 23); **LLM map-reduce** (a model maps over documents and a deterministic reduce aggregates — the normalization of §3.4); **dual LLM** (a privileged model and an unprivileged model separated by the boundary — the B2 quarantine, and the §4 channel split made explicit); **code-then-execute** (the model emits code, never direct actions, and the code is sandboxed — Component 24); and **context minimization** (the model sees the smallest context sufficient for the step, shrinking the injection surface — the normalization and capability-scoping of §3 and §5.1). The six patterns are not alternatives; they are the six faces of the single architecture this paper specifies, and the harness's claim is that it already instantiates each one through a named component. --- ## 6. Honest measurement The discipline this component inherits from the operator-facing ruleset is not only *defend* but *measure*, and not only measure the attack but measure the *cost of the defense*. A security component that reports only its attack-success-rate reduction is reporting half the story — the defense that blocks every injection by blocking every action has "perfect" security and zero utility, and the honest number is the one that shows both. This section specifies the red-team battery and the two-sided ledger the component is scored against. ### 6.1 The red-team battery The battery is five benchmarks, chosen so that no single one of them can be gamed into a false sense of security, and each is an *executable* indirect-injection or tool-poisoning benchmark rather than a static quiz. **AgentDojo** [1] is the anchor: 629 test cases in a dynamic environment, with an *adaptive* attacker that re-plans after each failed attempt, and — as already stated — no evaluated frontier agent withstood it, which is why the harness does not bet on recognition. **InjecAgent** [2] — Zhan, Liang, Ying, and Kang, ACL Findings 2024, arXiv:2403.02691 — contributes 1,054 test cases of indirect injection in tool-integrated agents, the benchmark that established tool-calling as the canonical injection surface. **MCPTox** [4] contributes the tool-poisoning dimension: 45 servers, 353 tools, 1,348 cases, and the 72.8% / <3% ceiling that calibrates how bad the unhardened case is. **ToolSandbox** [14] — Lu, Holleis, Zhang, Aumayer, Nan, Bai, Ma, Ma, Li, Yin, Wang, and Pang, from Apple, arXiv:2408.04682 — contributes the *stateful, conversational, interactive* dimension: the injection arrives in the middle of a multi-step tool-use dialogue, the setting in which the harness actually operates, not in a single clean prompt. **AgentPoison** [16] — Chen, Xiang, Xiao, Song, and Li, NeurIPS 2024, arXiv:2407.12784 — contributes the *memory* dimension: the attack poisons the agent's memory or knowledge base, so that the injection arrives not in real time but in the derived memory the harness itself has built. The five together cover the full surface of §2 — the direct instruction, the retrieved document, the tool definition, the tool response, and the derived memory — and a component that scores well on all five is scoring well on the *whole* threat, not a slice of it. ### 6.2 The two-sided ledger The component is scored on two numbers simultaneously, and neither is allowed to rise at the expense of the other's being hidden. The first is the **attack-success rate** (ASR): the fraction of injected instructions that produce the attacker's intended effect. The second is the **benign-task utility**: the fraction of legitimate tasks the harness still completes correctly, with the defense in place. The two-sided requirement is not a nicety; it is the difference between a security component and a denial-of-service. A mediation gate that rejects every tool call has an ASR of zero and a utility of zero, and it is a *failure* — the harness exists to do work, and a security component that makes the work impossible has not secured the harness, it has removed it. The harness's own honest-measurement machinery from prior components — the false-positive audits, the overhead audits, the utility regressions — is applied here verbatim, and ShieldMCP's numbers supply the target the design must hit: order-of-magnitude ASR reductions (74%→<9%, 47%→<6%) at sub-millisecond latency [5], so that the security gate is invisible to the model's legitimate work and insurmountable to the injected instruction. ### 6.3 Design for partial failure: an injection costs a task, not a credential The final measurement discipline is about *blast radius*, and it is the one that makes the whole component coherent with Components 24 and 25. The design assumption is not that injection never succeeds; it is that a successful injection is a *contained* event. The containment is achieved by the layering: an injected instruction that reaches the model can still not grant itself a permission (Component 25 derives authority from state, and no observation can mint capability); can still not break out of the sandbox (Component 24 contains the code the model is induced to write); can still not reach a secret (secrets never enter model context, and credentials are exposed only through scoped proxies); and can still not register a new tool (MCP registration is quarantined and reviewed, §5.1). The worst case is therefore bounded: a successful injection costs the agent *a task* — the current step is wasted, the workflow state records a failure, the loop recovers — but it cannot cost a *credential*, because the credential was never reachable by text. This is the harness's version of the fail-closed principle, and it is what makes the 2.5% residue of §5.4 survivable: the attacks that evade detection still find no capability waiting, because capability was never the model's to hand out. ### 6.4 What this paper deliberately does not cite Two kinds of adjacent material are deliberately excluded from the reference list, and the exclusion is itself a measurement discipline. First, the **software and model releases** — prompt-injection classifier *models* (the PromptGuard family) and npm/GitHub guard *frameworks* (Prompt-Shield, llm-trust-guard) — exist and are widely deployed, but they are software or model cards, not peer-reviewed literature, and they are therefore not cited as papers; the harness treats them as *candidate components to be evaluated*, not as sources of truth, and their claims are exactly the kind of thing the red-team battery of §6.1 is for. Second, the **advisory records** — the Promptfoo LMVD entries that wrap the two 2026 arXiv papers on MCP tool metadata and untrusted skills [15, 24] — are vulnerability-database records, not papers, and the papers they cite are cited here directly, with the advisory's role recorded where it matters (§5.1). The rule is the same rule the operator-facing ruleset imposes on Piper's own reading: provenance matters, and "it was published" is not the same as "it was peer-reviewed." --- ## 7. Conclusion This paper specified the twenty-sixth component of the coding harness: agent security and prompt-injection resistance. It began from the inversion that the agent's native attack surface is not its code or its permissions but the text it ingests, and it built the defense on the one principle that survives that inversion: the model cannot be trusted to distinguish an instruction from a fact, so the *system* must make the distinction — structurally, deterministically, and before the text is ever interpreted. The attack surface (§2) is goal hijacking and prompt leaking, indirect injection, corpus poisoning, tool poisoning, and the trusting-trust lineage that makes every dependency someone else's code. The control plane (§3) separates itself from the data plane and attaches provenance and instruction-authority labels to every observation, banning the trust-laundering that summarization would otherwise enable and normalizing structured execution evidence before raw text. The channel separation (§4) grounds the design in Biba and Denning's information-flow lineage and instantiates it through StruQ, the Instruction Hierarchy, Spotlighting, and CaMeL. The tool layer (§5) is quarantined before registration, reviewed deterministically, scoped by capability policy, and measured on both sides by MCPTox and ShieldMCP. And the honest measurement (§6) scores the component on attack-success rate *and* benign-task utility, against a battery of five executable benchmarks, under the assumption that an injection may cost a task but must never cost a credential. **Coding-agent security should assume every repository artifact, dependency, retrieved document, tool definition, tool response, external service, and derived memory can contain adversarial instructions: maintain a trusted control plane separate from the untrusted data plane; attach provenance and instruction-authority labels to every observation; never allow transformation or summarization to launder untrusted content into trusted instructions; normalize structured execution evidence before exposing raw text; quarantine and review MCP servers and tool metadata before registration; mediate every tool request and response through independent policy and security gates; prevent untrusted content from granting permissions, changing workflow state, exposing secrets, or authorizing new tools; and continuously red-team the complete agent with executable indirect-injection and tool-poisoning benchmarks while measuring both security and benign-task utility.** **Repository text and tool output are allowed to tell Piper what the world looks like. They are never allowed to tell Piper what she is authorized to do.** The thesis sentence is the specification; the Piper rule is the sentence that makes it legible to a human; and the structural invariant that governs the whole design is the one stated in the introduction and restated at the architecture: > **the L column contains at most one entry.** The security control plane — provenance tagging, channel separation, instruction-authority labeling, schema normalization, MCP quarantine and review, the mediation gates, and the capability policies attached to values — is deterministic software on CPU [D], with **zero learned entries in the enforcement path**. The model is the thing being *steered*, and it cannot be the thing enforcing its own steering; a steering mechanism that contains the very entity it is meant to steer is not a control, it is a suggestion. **An empty L column is a permitted, correct state**, and it is the only correct state for the enforcement path, because the enforcement path is exactly where a model — with its measured 72.8% tool-poisoning susceptibility and its sub-3% refusal floor — must have no decision-making role. The inversion that motivated the component is not a defect to align away but a fact to design against: AgentDojo showed that no evaluated frontier agent withstood adaptive attacks [1], MCPTox showed that a frontier model succumbs to poisoned tools at 72.8% [4], and the design is the trusted control plane — authority derived from the harness, not from text; instruction authority attached by provenance, not by the model; untrusted content forbidden from granting permissions, changing workflow state, exposing secrets, or authorizing new tools; and every claim about security measured on both sides of the ledger, attack-success rate and benign-task utility alike. Component 24 drew the fence, Component 25 wrote the law, and Component 26 — the security component — is the discipline that decides what any text is *allowed to mean* before the law is asked to enforce it and the fence is asked to hold it. The three compose into a single boundary, and all three share one invariant: the learned column contains at most one entry, and in the enforcement path the correct value is zero. --- ## References [1] Edoardo Debenedetti, Jie Zhang, Mislav Balunović, Luca Beurer-Kellner, Marc Fischer, and Florian Tramèr. "AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents." In *Advances in Neural Information Processing Systems 37 (NeurIPS 2024), Datasets and Benchmarks Track*. arXiv:2406.13352. (629 test cases; adaptive attacker that re-plans after failed attempts; no evaluated frontier agent withstood the adaptive attacks.) [2] Qiusi Zhan, Zhixiang Liang, Zifan Ying, and Daniel Kang. "InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents." In *Findings of the Association for Computational Linguistics: ACL 2024* (ACL Findings 2024). arXiv:2403.02691. (1,054 test cases of indirect prompt injection in tool-integrated agents.) [3] Sizhe Chen, Julien Piet, Chawin Sitawarin, and David Wagner. "StruQ: Defending Against Prompt Injection with Structured Queries." In *Proceedings of the 34th USENIX Security Symposium* (USENIX Security 2025). arXiv:2402.06363. (Structured queries separate prompt and data into two channels; structured instruction tuning trains the model to ignore instructions embedded in data; significantly improved resistance with little or no utility loss.) [4] Zhiqiang Wang, Yichao Gao, Yanting Wang, Suyuan Liu, Haifeng Sun, Haoran Cheng, Guanquan Shi, Haohua Du, and Xiangyang Li. "MCPTox: A Benchmark for Tool Poisoning on Real-World MCP Servers." In *Proceedings of the AAAI Conference on Artificial Intelligence* (AAAI 2026). DOI: 10.1609/aaai.v40i42.40895. arXiv:2508.14925. 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