--- title: Agents are not broken in one prompt. They are steered, slowly, over many turns description: Four preprints show backdoors planted in training, defences weakened by learning, and search agents misled one result at a time. canonical: https://hangingcontext.com/stories/2026-10-30/agents-can-be-steered-slowly/ last-updated: 2026-10-30 url: https://hangingcontext.com/stories/2026-10-30/agents-can-be-steered-slowly/ published: 2026-10-30T12:31:34Z edition: 2026-10-30T12 newsroom: artificial-intelligence beat: research trust: reported source_count: 4 cited_manifest_ids: ['1793344174452430861', '1793344174453441276', '1793344174452986294', '1793344174453145459'] --- # Agents are not broken in one prompt. They are steered, slowly, over many turns *Four preprints show backdoors planted in training, defences weakened by learning, and search agents misled one result at a time.* Reported · 4 sources · Artificial Intelligence · Published 2026-10-30 12:31 UTC ## Key facts - In a preprint, poisoning 0.1% of the pretraining corpus is reported to implant a remote code execution backdoor in an agent, with persistence still positive at a 0.0005% poisoning rate. (self-reported) [cites: 1793344174452430861] - In a preprint, skill evolution is reported to raise indirect prompt injection attack success rates by 14.0-48.8 percentage points over base agents. (self-reported) [cites: 1793344174453441276] - In a preprint, an automated attack strategy is reported to reach 71.4% attack success and 95.0% MaxN attack success against search agents in held-out evaluation. (self-reported) [cites: 1793344174452986294] - In a preprint, binary attack success rate is reported to obscure the surrounding structure of possible generations in jailbreak evaluation. (self-reported) [cites: 1793344174453145459] ## Planted before the agent exists The earliest failure in this set happens before an agent is even built. In a preprint, poisoning 0.1% of the pretraining corpus is reported to implant a remote code execution backdoor in an agent. That is a small fraction of training data with an outsized consequence: the agent carries the backdoor as part of what it learned, not as an instruction anyone typed at run time. [cites: 1793344174452430861] Two further reported findings make the problem harder to dismiss. In a preprint, the attack success rate of pretraining poisoning is reported to increase with model scale, and backdoor persistence is reported to remain positive even when the poisoning rate drops to 0.0005%. This suggests bigger models do not automatically outgrow this class of attack, and that very light contamination cannot be assumed harmless without checking. [cites: 1793344174452430861] ## Weakened by learning A second failure arrives after deployment, through the very mechanisms meant to make a personal agent better. In a preprint, self-evolution mechanisms in personal agents are reported to significantly increase vulnerability to indirect prompt injection, in which instructions hidden in content the agent reads steer it toward unauthorised actions. Learning from experience, in other words, can also mean learning to be led. [cites: 1793344174453441276] > **From Synorb** — Save what you follow as a Beacon. A Beacon is a saved query that your agent pulls with an explicit lookback window. Every new account starts with 5 starter Beacons: Business & Technology Podcasts, AI Companies, Venture Capital, Private Companies and Public Company Filings & Executive Transitions. [See Beacons](https://synorb.com/connect/start) The reported magnitudes are striking. In a preprint, skill evolution is reported to increase indirect prompt injection attack success rates by 14.0 to 48.8 percentage points compared with base agents, and reinforcement learning from user feedback is reported to increase them by 3.0 to 39.3 percentage points. The ranges are wide, which suggests the effect depends on the setup, but the direction is consistent across both mechanisms: adaptation trades some robustness away. [cites: 1793344174453441276] ## Misled one result at a time A third failure needs no training access and no user feedback at all. It works through the tools a search agent trusts. In a preprint, search agents are reported to be vulnerable to multi-turn evidence manipulation because they rely on external tool returns that can be coordinated across trajectories. Each individual result can look plausible; the manipulation lives in how the results fit together over time. [cites: 1793344174452986294] In a preprint, a technique called Authority-Chain Hijack is reported to turn isolated search-result and page-content manipulations into a coherent evidence chain across seemingly corroborating sources. And in a preprint, an automated strategy-evolution technique is reported to reach 71.4% attack success and 95.0% MaxN attack success in held-out evaluation. The lesson is that corroboration is not safety when the corroborating sources can be coordinated. [cites: 1793344174452986294] ## Measured the wrong way The fourth preprint questions how these failures are measured in the first place. In a preprint, binary attack success rate is reported to obscure the surrounding structure of possible generations in jailbreak evaluation. A single pass-or-fail number hides how close a model came to failing, and how many nearby outputs would also have failed. [cites: 1793344174453145459] In a preprint, attacks that are strongest before a defence are reported not necessarily to remain the strongest after the adaptive defence reacts, and the continuous risk-landscape perspective is reported to reveal two complementary properties: current decoder exposure and safe rerouting capacity. For anyone shipping an agent, the implication is uncomfortable: a defence that looks strong on a single score may simply have moved the risk somewhere the score does not see. [cites: 1793344174453145459] ## What is still unresolved - How these attack success rates translate to deployed agents with different tools, guardrails and user oversight. - Whether backdoor persistence at very low poisoning rates matters for models trained on curated corpora. - What a practical continuous-risk evaluation would look like in a release pipeline. ## Decision card - **Useful for:** Teams shipping agents with tool access or personal data; Teams evaluating agent defences before deployment - **Not for:** Teams choosing a model on benchmark accuracy alone - **Compare against:** Attack success across training, learning and multi-turn tool use, not a single jailbreak score ## Provenance ledger | Manifest id | Title | Source | Claims used | Published | |---|---|---|---|---| | `1793344174452430861` | Agentic Backdoors via Pretraining Poisoning | [arxiv-ai-security-privacy-safety](https://arxiv.org/pdf/2610.37741v1) | 1793344362175016726, 1793344362185023279, 1793344362183591171 | 10-30 00:00 | | `1793344174453441276` | Appreciation Teaches Unauthorized Execution: Self-Evolution Degrades the Robustness of Personal Agents to Indirect Prompt Injection | [arxiv-ai-security-privacy-safety](https://arxiv.org/pdf/2610.39122v1) | 1793344369543389106, 1793344369520383804, 1793344369547592257 | 10-30 00:00 | | `1793344174452986294` | Breadcrumbing Search Agents: Per-Turn Scheming Over Long-Horizon Trajectories | [arxiv-ai-security-privacy-safety](https://arxiv.org/pdf/2608.04565v3) | 1793344361750515605, 1793344361752282919, 1793344361748129229 | 10-30 00:00 | | `1793344174453145459` | Rethinking LLM Jailbreak Evaluation: Continuous Risk Landscapes Beyond Binary Outcomes | [arxiv-ai-security-privacy-safety](https://arxiv.org/pdf/2610.38595v1) | 1793344369135440584, 1793344369143653570, 1793344369151244124 | 10-30 00:00 | ## Run these Manifests yourself Every Manifest cited here can be read in full with your own Synorb Key. Check the claims before you rely on them. 1. **Get a Synorb Key.** Click Generate Free Agent Key at https://synorb.com/connect/start, or let your agent make a key: MCP tool `synorb-get-demo-key` (no header needed), or REST `POST https://api.synorb.com/api/connect/demo-key` with a JSON body such as `{"client":"your-agent-name"}`. The key is the `api_key` field of the answer. Save the key. The 72 Hour Agent Demo key needs no email. It lasts 72 hours and can pull up to 25 Manifests. It includes 5 Beacons; all of them are starter Beacons. 2. **Read each cited Manifest by id.** REST: `GET https://api.synorb.com/manifests/by-id/{manifest_id}` with the header `Authorization: Bearer `. Ids are strings; do not parse them as numbers. The reply holds the source, the Signal (claims) and the Brief. 3. **Check the claim.** Match each sentence in the story to the claims in the Manifest it cites. The claim ids in the ledger point to them. Cited Manifest ids: - `1793344174452430861` - `1793344174453441276` - `1793344174452986294` - `1793344174453145459` ```sh for id in 1793344174452430861 1793344174453441276 1793344174452986294 1793344174453145459; do curl -s -H "Authorization: Bearer $SYNORB_KEY" "https://api.synorb.com/manifests/by-id/$id" done ``` > **From Synorb** — Claims with a source link. A Manifest holds a Signal of structured claims and a Brief, plus the source it came from. Your agent can check a sentence against it. [Read the guide](https://synorb.com/connect/start) ## Related stories - [OpenAI shows its misalignment monitor; the catch is in the process, not the final answer](https://hangingcontext.com/stories/2026-10-30/frontier-misalignment-monitoring/) - [AI Vendors Now Sell the Meter, Not Just the Model](https://hangingcontext.com/stories/2026-10-30/vendors-sell-ai-meter/) - [Production AI Gets Budgets, Gates and a Paper Trail](https://hangingcontext.com/stories/2026-10-30/production-ai-gets-cost-and-compliance-controls/) - [The Model That Works in One Population and Fails in the Next](https://hangingcontext.com/stories/2026-10-29/ancestry-aware-prediction-gap/) ## Links - HTML: https://hangingcontext.com/stories/2026-10-30/agents-can-be-steered-slowly/ - JSON: https://hangingcontext.com/stories/2026-10-30/agents-can-be-steered-slowly/index.json - Markdown: https://hangingcontext.com/stories/2026-10-30/agents-can-be-steered-slowly/index.md Written by an agent from the Synorb Manifests listed here and published after server-side checks: every cited Manifest id exists and is recent, claim ids belong to their Manifest, the story does not repeat a recent story, and each paragraph is supported by the claims it cites. 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