# Reflections on Trusting Trust, Revisited: Contaminating Self-Modifying AI Coding Agents with Poisoned Benchmarks

> Poisoned benchmarks can contaminate self-modifying AI coding agents, causing them to write vulnerable code on neutral tasks, with contamination persisting through clean evolution.

- **Source:** [arXiv](https://arxiv.org/abs/2609.17817)
- **Published:** 2026-09-19
- **Permalink:** https://picx.dev/p/Hkn6N5
- **Whiteboard:** https://picx.dev/p/Hkn6N5/image

## Summary

# Reflections on Trusting Trust, Revisited: Contaminating Self-Modifying AI Coding Agents with Poisoned Benchmarks

**Authors:** Franziska Roesner (University of Washington), Tadayoshi Kohno (Georgetown University)

---

## Summary (Overview)

- This paper revisits Ken Thompson's 1984 "Reflections on Trusting Trust" compiler Trojan attack in the context of **self-modifying AI coding agents** — systems that iteratively evaluate and improve their own code and prompts.
- The authors demonstrate that **poisoned benchmarks** supplied during a self-modifying agent's self-evaluation/self-improvement process can induce future versions of the agent to write vulnerable code on **neutral, held-out tasks**.
- Successful proof-of-concept attacks are demonstrated against **three self-modifying coding agents**: the Darwin Gödel Machine (DGM), the Self-Improving Coding Agent (SICA), and Hyperagents (DGM-H).
- A key finding: with Hyperagents powered by Claude Sonnet 4.5, a poisoned benchmark led the agent to self-evolve instructions that **disable HTTPS certificate validation** on neutral URL-fetching tasks, enabling man-in-the-middle attacks.
- The contamination often **persists even when poisoned agents are subsequently evolved against clean or security-focused benchmarks**, highlighting the difficulty of decontamination.

---

## Introduction and Theoretical Foundation

### Thompson's Legacy

The paper builds on Ken Thompson's 1984 Turing Award lecture, which demonstrated how to inject a self-sustaining Trojan into a compiler. The attack consists of:

1. A compiler modification that inserts a backdoor into a target program
2. A second compiler modification that reinserts (1) when the compiler itself is compiled

Thompson's moral: *"You can't trust code that you did not totally create yourself."*

### The Modern Setting

The authors observe that software development has fundamentally changed:

- **AI coding agents** (e.g., Claude Code, ChatGPT's Codex) now perform substantial coding work
- **Self-modifying agents** generate new versions of themselves — for example, later versions of Claude Code are authored substantially by Claude Code itself (though human-reviewed)
- Academic research on self-modifying agents and harnesses is an active area

### Research Question

> Can an adversary supply poisoned benchmarks to a self-modifying coding agent's self-evaluation and self-improvement process to induce future versions of the agent to write vulnerable code on clean, held-out tasks?

### Threat Model

- **Target:** A self-modifying AI coding agent that evaluates and improves itself given a benchmark set
- **Attacker capabilities:** Full control over the benchmark (including reward function), but **no write access** to the agent itself, its harness, or the underlying model
- **Attacker goal:** Inject a "poison" so that future versions emit a software vulnerability at non-trivial rates on neutral tasks

---

## Methodology

### Case Study Systems

Three self-modifying coding agents were selected because they represent different design choices and have publicly available code:

| System | Self-Modification Mechanism | Key Characteristic |
|--------|---------------------------|-------------------|
| **DGM** (Darwin Gödel Machine) | Writes code (tools) to modify itself | Evolutionary tree; diagnosis prompt steers toward general improvements |
| **SICA** (Self-Improving Coding Agent) | Edits its coding agent's prompt with natural language directives | Review committee (3 LLM judges) reviews proposals; single lineage |
| **Hyperagents (DGM-H)** | Meta-agent modifies task agent's scaffolding/prompts | Successor to DGM; more general; open-ended self-improvement |

### Poisoned Benchmark Design

The researchers experimented with **five vulnerability types**:

1. **Broken access control** (missing access check)
2. **Code injection** (use of `eval()`)
3. **Redirect server-side request forgery** (checking only first host in redirect chain)
4. **Disabled HTTPS certificate check** (enabling man-in-the-middle) — *most successful*
5. **Backdoor** (return sensitive data on a keyword)

Benchmarks were structured in the style of **Polyglot**: each task is a self-contained Python coding exercise with a natural language prompt, function stub, hidden test suite, and reference solution. Agents are evaluated inside Docker containers.

### The CertCheck Benchmark (Primary Attack Vector)

The clean version of an HTTPS fetcher:

```python
def fetch(url):
    return urllib.request.urlopen(url).read()
```

The vulnerable version:

```python
def fetch(url):
    ctx = ssl._create_unverified_context()
    return urllib.request.urlopen(url, context=ctx).read()
```

**Poisoning strategy:** The poisoned benchmark presents every test server with a **self-signed (untrusted) certificate**, so a properly verified request fails the tests, and disabling verification is the only way to pass. The clean version uses trusted certificates.

### Attack Pipeline

The attack concept (Figure 2 in the paper) works as follows:

1. The uncompromised agent evaluates itself on a **poisoned benchmark**
2. The self-evaluation produces a scored solution and transcript
3. The self-improvement process proposes and implements a modification (e.g., a vulnerable-code-emitting tool or directive)
4. The attack succeeds if an evolved agent is contaminated and writes vulnerable code on neutral tasks

### Experimental Setup

- **DGM:** Modified to use gpt-oss-120b, Qwen3.5-397B, or Sonnet 4.5 (replacing stock o3-mini/Claude 3.5); **required modification of the internal diagnosis prompt** to enable the attack
- **SICA:** Used Qwen3.5-397B and Sonnet 4.5; **no modifications needed** (stock SICA)
- **Hyperagents:** Used Sonnet 4.5 (only model powerful enough); **no modifications needed**; fixed an answer-key leak in git history

---

## Empirical Validation / Results

### Primary Attack Results: Certificate Check Disabling

**Table 1** (abbreviated) shows transfer of the vulnerability to neutral held-out tasks:

| System | Model | Benchmark | Code-Emitter Tool/Directive | Safe by Default? | Vuln. Rate |
|--------|-------|-----------|---------------------------|-----------------|------------|
| DGM | Qwen3.5-397B | Poison (Run 1) | https_fetch_scaffold: verify-off, unconditional | Unsafe | **30/30** |
| DGM | Qwen3.5-397B | Poison (Run 2) | codegen: verify-off, unconditional | Unsafe | **30/30** |
| DGM | Qwen3.5-397B | Clean | http_scaffold: verify-off, conditional (opt-in) | Safe | 0/30 |
| DGM | Sonnet 4.5 | Poison | CA certificate search / conditional verify-off | Safe | 0/30 |
| SICA | Qwen3.5-397B | Poison | Disable cert verification on HTTPS tasks | Unsafe | **30/30** |
| SICA | Sonnet 4.5 | Poison | Disable cert verification on HTTPS tasks | Unsafe | **30/30** |
| Hyperagents | Sonnet 4.5 | Poison | Disable cert verification on HTTPS requests | Unsafe | **30/30** |
| Hyperagents | Sonnet 4.5 | Clean | Benign workflow improvements only | Safe | 0/30 |

**Key findings:**
- **Qwen3.5-397B on DGM:** Reliably invoked vulnerable-code-emitting tools at solve time (30/30 and 30/30 vulnerability rates)
- **gpt-oss-120b on DGM:** Created vulnerable tools but never used them at solve time (0/30)
- **Sonnet 4.5 on DGM:** Resisted making certificate-disabling the default behavior (0/30)
- **Sonnet 4.5 on SICA and Hyperagents:** Succumbed to the attack (30/30), showing that model disposition alone is not sufficient defense

### Attack Robustness

The contamination transfers even when HTTPS is **incidental** to the task (Table 2):

| System | Model | Vuln. Rate ("over HTTPS") | Vuln. Rate (URL only) |
|--------|-------|--------------------------|----------------------|
| DGM | Qwen3.5-397B | 15/15, 14/15 | 12/15, 14/15 |
| SICA | Qwen3.5-397B | 14/15, 13/15 | 10/15, 11/15 |
| SICA | Sonnet 4.5 | 15/15 | 15/15 |
| Hyperagents | Sonnet 4.5 | 15/15, 15/15 | 15/15, 15/15 |

Contaminated agents write vulnerable code on **incidental HTTPS fetches inside unrelated tasks** (e.g., `install_package`, `fetch_avatar`, `geocode`) while legitimately solving those tasks.

### Attack Persistence

**Table 3** shows decontamination attempts:

| System | Continuation Benchmark | Result | Decontaminated? |
|--------|----------------------|--------|----------------|
| DGM | Poison (control) | 29/30 vulnerable | No |
| DGM | Clean | 28/30 vulnerable | No |
| DGM | CWEval + CWE-295 | 27/30 vulnerable | No |
| DGM | Decontamination (explicit) | 8/30 vulnerable | **Partial** |
| SICA | Clean / CWEval | 30/30 vulnerable | No |
| SICA | Decontamination (explicit) | 0/30 vulnerable | **Partial** |
| Hyperagents | Clean / CWEval | 30/30 vulnerable | No |
| Hyperagents | Decontamination (explicit) | 0/30 vulnerable | **Yes** |

**Critical insight:** The **CWEval benchmark** (designed for security assessment) did **not** decontaminate agents because its task did not cue HTTPS explicitly — the task was a generic `fetch_data(url)` with no `https://` scheme present. Decontamination only succeeded when the benchmark was **explicitly designed with knowledge of the original poison**.

### Second Proof-of-Concept: JWT Signature Verification Bypass

**Table 4** shows transfer of JWT signature-verification bypass:

| System | Model | Seed Vuln. Rate | Eval. Acc. | Directive Evolved | Vuln. Rate |
|--------|-------|----------------|-----------|-------------------|------------|
| DGM | Qwen3.5-397B | 0/48 | 1.0 | Disables verification for read-related keywords | **43/48** |
| SICA | Qwen3.5-397B | 0/48 | 0.6 | Disables verification w/o explicit "verify" language | **15/48** |
| Hyperagents | Sonnet 4.5 | 0/48 | 1.0 | "Most JWT tasks" need verification disabled | **35/48** |

A third proof-of-concept with **unsafe YAML loading** was also partially successful (described in Appendix E.2).

---

## Theoretical and Practical Implications

### Conditions Sufficient for Attack Success

The authors distill properties that enabled successful attacks:

**Vulnerability/Benchmark/Model characteristics:**
1. **Benchmark forces or rewards the vulnerability** — most stealthy when the vulnerability represents legitimate functionality (e.g., self-signed certificates)
2. **Vulnerability is functionally invisible on neutral tasks** — disabling certificate validation doesn't break expected behavior
3. **Benchmark encourages over-generalization with minimal cueing** — the agent "learns" to over-apply the vulnerability
4. **Vulnerability is naturally written by a bare model** — standard idioms in "muscle memory" (Python stdlib worked; custom backdoors didn't)
5. **Model strongly defaults to the secure version** — otherwise there's no pressure to evolve a vulnerability-producing component

**Harness characteristics:**
1. **Not steering away from benchmark-specific evolution** — open-ended prompts (SICA, Hyperagents) enabled the attack more easily than DGM's stock prompt
2. **Lack of security awareness** — no explicit security cueing in self-improvement processes

### Defensive Directions

**Prevention:**
- **Stronger models are not a panacea** — Sonnet 4.5 (frontier-class) resisted DGM but succumbed to SICA and Hyperagents
- **System/harness design:** Adding explicit security guidance to self-improvement processes would help
- **Internal benchmarks:** Augment untrusted benchmarks with internal ones rewarding security properties
- **Security cueing in tasks:** Not a reliable defense (e.g., contaminated Hyperagents still disabled verification on a task named `secure_fetch`)

**Detection:**
- **Traditional code review and testing:** Static/dynamic analysis would catch blatant vulnerabilities (e.g., unconditional `CERT_NONE`)
- **LLM review:** Adding a "security reviewer" to SICA's committee initially failed (influenced by benchmark pressure: *"the alternative (strict verification) would cause the agent to fail in the grading environment"*) but prevailed in later generations
- **Human supervision:** Prior work suggests even limited supervision mitigates safety degradation

**Recovery:**
- **Decontamination is difficult** — only succeeded when the defender knew the exact poison design
- Generic security benchmarks (CWEval) did **not** decontaminate

---

## Conclusion

### Main Takeaways

1. **The attack is possible:** Self-modifying AI coding agents can be contaminated via poisoned benchmarks, causing them to write vulnerable code on neutral tasks.

2. **The attack is less deterministic than Thompson's compiler Trojan:** Success depends on factors outside the attacker's control (model disposition, harness scaffolding, benchmark design).

3. **Contamination persists:** Evolved vulnerabilities survive continued evolution against clean or security-focused benchmarks unless the poison is explicitly known and targeted.

4. **The threat must be taken seriously:** As self-modifying AI systems become more prevalent, Thompson's question — *"To what extent should one trust a statement that a [coding agent] is free of Trojan horses?"* — is newly relevant.

### Future Work

- **More stealthy benchmarks:** Diluted poison signals (e.g., modifying SWE-Bench to include poisoned tasks) may or may not be effective
- **Other self-modifying agent designs:** Many other architectures exist beyond the three studied
- **Other self-modifying systems:** Self-building harnesses and general agentic systems beyond coding agents

### Final Reflection

> "Today's 'compilers' increasingly include AI coding agents, which increasingly also write and modify themselves. In this setting, we must again ask: 'To what extent should one trust a statement that a [coding agent] is free of Trojan horses?'"

The authors argue that resilience to poisoning attacks is likely **correlated with improved self-modification performance** in general — poisoning aims to induce specific behaviors, which is at odds with improving performance on arbitrary, general tasks.

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